Overview

Dataset statistics

Number of variables41
Number of observations112372
Missing cells173691
Missing cells (%)3.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory35.2 MiB
Average record size in memory328.0 B

Variable types

Numeric14
Categorical27

Alerts

date_initial has constant value "2016-09-04"Constant
dummy has constant value "1"Constant
customer_id has a high cardinality: 97917 distinct valuesHigh cardinality
customer_unique_id has a high cardinality: 94721 distinct valuesHigh cardinality
customer_city has a high cardinality: 4108 distinct valuesHigh cardinality
order_id has a high cardinality: 97917 distinct valuesHigh cardinality
order_purchase_timestamp has a high cardinality: 97371 distinct valuesHigh cardinality
order_approved_at has a high cardinality: 89534 distinct valuesHigh cardinality
order_delivered_carrier_date has a high cardinality: 80450 distinct valuesHigh cardinality
order_delivered_customer_date has a high cardinality: 95022 distinct valuesHigh cardinality
order_estimated_delivery_date has a high cardinality: 450 distinct valuesHigh cardinality
product_id has a high cardinality: 32789 distinct valuesHigh cardinality
seller_id has a high cardinality: 3090 distinct valuesHigh cardinality
shipping_limit_date has a high cardinality: 92643 distinct valuesHigh cardinality
review_id has a high cardinality: 97709 distinct valuesHigh cardinality
review_comment_title has a high cardinality: 4497 distinct valuesHigh cardinality
review_comment_message has a high cardinality: 35692 distinct valuesHigh cardinality
review_creation_date has a high cardinality: 633 distinct valuesHigh cardinality
review_answer_timestamp has a high cardinality: 97547 distinct valuesHigh cardinality
product_category_name has a high cardinality: 73 distinct valuesHigh cardinality
date_achat has a high cardinality: 616 distinct valuesHigh cardinality
Unnamed: 0 is highly overall correlated with diff_days and 2 other fieldsHigh correlation
customer_zip_code_prefix is highly overall correlated with customer_stateHigh correlation
price is highly overall correlated with product_weight_g and 1 other fieldsHigh correlation
product_weight_g is highly overall correlated with price and 3 other fieldsHigh correlation
product_length_cm is highly overall correlated with product_weight_g and 1 other fieldsHigh correlation
product_height_cm is highly overall correlated with product_weight_gHigh correlation
product_width_cm is highly overall correlated with product_weight_g and 1 other fieldsHigh correlation
diff_days is highly overall correlated with Unnamed: 0 and 2 other fieldsHigh correlation
score_rfm is highly overall correlated with Unnamed: 0 and 2 other fieldsHigh correlation
customer_state is highly overall correlated with customer_zip_code_prefixHigh correlation
score_rec is highly overall correlated with Unnamed: 0 and 1 other fieldsHigh correlation
order_status is highly imbalanced (93.4%)Imbalance
score_freq is highly imbalanced (94.2%)Imbalance
order_delivered_carrier_date has 1184 (1.1%) missing valuesMissing
order_delivered_customer_date has 2360 (2.1%) missing valuesMissing
review_comment_title has 98938 (88.0%) missing valuesMissing
review_comment_message has 64730 (57.6%) missing valuesMissing
product_category_name has 1598 (1.4%) missing valuesMissing
product_name_lenght has 1598 (1.4%) missing valuesMissing
product_description_lenght has 1598 (1.4%) missing valuesMissing
product_photos_qty has 1598 (1.4%) missing valuesMissing
Unnamed: 0 is uniformly distributedUniform
customer_id is uniformly distributedUniform
customer_unique_id is uniformly distributedUniform
order_id is uniformly distributedUniform
order_purchase_timestamp is uniformly distributedUniform
order_approved_at is uniformly distributedUniform
order_delivered_carrier_date is uniformly distributedUniform
order_delivered_customer_date is uniformly distributedUniform
shipping_limit_date is uniformly distributedUniform
review_id is uniformly distributedUniform
review_answer_timestamp is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique

Reproduction

Analysis started2023-02-10 10:44:50.724028
Analysis finished2023-02-10 10:45:54.216642
Duration1 minute and 3.49 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct112372
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56185.5
Minimum0
Maximum112371
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size878.0 KiB
2023-02-10T11:45:54.351665image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5618.55
Q128092.75
median56185.5
Q384278.25
95-th percentile106752.45
Maximum112371
Range112371
Interquartile range (IQR)56185.5

Descriptive statistics

Standard deviation32439.147
Coefficient of variation (CV)0.57735798
Kurtosis-1.2
Mean56185.5
Median Absolute Deviation (MAD)28093
Skewness0
Sum6.313677 × 109
Variance1.0522982 × 109
MonotonicityStrictly increasing
2023-02-10T11:45:54.541902image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
74911 1
 
< 0.1%
74922 1
 
< 0.1%
74921 1
 
< 0.1%
74920 1
 
< 0.1%
74919 1
 
< 0.1%
74918 1
 
< 0.1%
74917 1
 
< 0.1%
74916 1
 
< 0.1%
74915 1
 
< 0.1%
Other values (112362) 112362
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
112371 1
< 0.1%
112370 1
< 0.1%
112369 1
< 0.1%
112368 1
< 0.1%
112367 1
< 0.1%
112366 1
< 0.1%
112365 1
< 0.1%
112364 1
< 0.1%
112363 1
< 0.1%
112362 1
< 0.1%

customer_id
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct97917
Distinct (%)87.1%
Missing0
Missing (%)0.0%
Memory size878.0 KiB
be1c4e52bb71e0c54b11a26b8e8d59f2
 
22
fc3d1daec319d62d49bfb5e1f83123e9
 
21
be1b70680b9f9694d8c70f41fa3dc92b
 
20
10de381f8a8d23fff822753305f71cae
 
15
adb32467ecc74b53576d9d13a5a55891
 
15
Other values (97912)
112279 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3595904
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique87758 ?
Unique (%)78.1%

Sample

1st row08c5351a6aca1c1589a38f244edeee9d
2nd row08c5351a6aca1c1589a38f244edeee9d
3rd row683c54fc24d40ee9f8a6fc179fd9856c
4th row86dc2ffce2dfff336de2f386a786e574
5th row86dc2ffce2dfff336de2f386a786e574

Common Values

ValueCountFrequency (%)
be1c4e52bb71e0c54b11a26b8e8d59f2 22
 
< 0.1%
fc3d1daec319d62d49bfb5e1f83123e9 21
 
< 0.1%
be1b70680b9f9694d8c70f41fa3dc92b 20
 
< 0.1%
10de381f8a8d23fff822753305f71cae 15
 
< 0.1%
adb32467ecc74b53576d9d13a5a55891 15
 
< 0.1%
a7693fba2ff9583c78751f2b66ecab9d 14
 
< 0.1%
d5f2b3f597c7ccafbb5cac0bcc3d6024 14
 
< 0.1%
7d321bd4e8ba1caf74c4c1aabd9ae524 13
 
< 0.1%
0d93f21f3e8543a9d0d8ece01561f5b2 12
 
< 0.1%
3b54b5978e9ace64a63f90d176ffb158 12
 
< 0.1%
Other values (97907) 112214
99.9%

Length

2023-02-10T11:45:54.777700image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
be1c4e52bb71e0c54b11a26b8e8d59f2 22
 
< 0.1%
fc3d1daec319d62d49bfb5e1f83123e9 21
 
< 0.1%
be1b70680b9f9694d8c70f41fa3dc92b 20
 
< 0.1%
10de381f8a8d23fff822753305f71cae 15
 
< 0.1%
adb32467ecc74b53576d9d13a5a55891 15
 
< 0.1%
a7693fba2ff9583c78751f2b66ecab9d 14
 
< 0.1%
d5f2b3f597c7ccafbb5cac0bcc3d6024 14
 
< 0.1%
7d321bd4e8ba1caf74c4c1aabd9ae524 13
 
< 0.1%
9eb3d566e87289dcb0acf28e1407c839 12
 
< 0.1%
daf15f1b940cc6a72ba558f093dc00dd 12
 
< 0.1%
Other values (97907) 112214
99.9%

Most occurring characters

ValueCountFrequency (%)
f 225399
 
6.3%
c 225289
 
6.3%
5 225194
 
6.3%
1 225081
 
6.3%
6 224917
 
6.3%
8 224874
 
6.3%
2 224823
 
6.3%
7 224778
 
6.3%
a 224719
 
6.2%
b 224709
 
6.2%
Other values (6) 1346121
37.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2246846
62.5%
Lowercase Letter 1349058
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 225194
10.0%
1 225081
10.0%
6 224917
10.0%
8 224874
10.0%
2 224823
10.0%
7 224778
10.0%
9 224677
10.0%
3 224639
10.0%
4 223963
10.0%
0 223900
10.0%
Lowercase Letter
ValueCountFrequency (%)
f 225399
16.7%
c 225289
16.7%
a 224719
16.7%
b 224709
16.7%
e 224522
16.6%
d 224420
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 2246846
62.5%
Latin 1349058
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
5 225194
10.0%
1 225081
10.0%
6 224917
10.0%
8 224874
10.0%
2 224823
10.0%
7 224778
10.0%
9 224677
10.0%
3 224639
10.0%
4 223963
10.0%
0 223900
10.0%
Latin
ValueCountFrequency (%)
f 225399
16.7%
c 225289
16.7%
a 224719
16.7%
b 224709
16.7%
e 224522
16.6%
d 224420
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3595904
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 225399
 
6.3%
c 225289
 
6.3%
5 225194
 
6.3%
1 225081
 
6.3%
6 224917
 
6.3%
8 224874
 
6.3%
2 224823
 
6.3%
7 224778
 
6.3%
a 224719
 
6.2%
b 224709
 
6.2%
Other values (6) 1346121
37.4%

customer_unique_id
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct94721
Distinct (%)84.3%
Missing0
Missing (%)0.0%
Memory size878.0 KiB
d97b3cfb22b0d6b25ac9ed4e9c2d481b
 
24
c8460e4251689ba205045f3ea17884a1
 
24
4546caea018ad8c692964e3382debd19
 
21
c402f431464c72e27330a67f7b94d4fb
 
20
0f5ac8d5c31de21d2f25e24be15bbffb
 
18
Other values (94716)
112265 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3595904
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique82917 ?
Unique (%)73.8%

Sample

1st rowb7d76e111c89f7ebf14761390f0f7d17
2nd rowb7d76e111c89f7ebf14761390f0f7d17
3rd row4854e9b3feff728c13ee5fc7d1547e92
4th row830d5b7aaa3b6f1e9ad63703bec97d23
5th row830d5b7aaa3b6f1e9ad63703bec97d23

Common Values

ValueCountFrequency (%)
d97b3cfb22b0d6b25ac9ed4e9c2d481b 24
 
< 0.1%
c8460e4251689ba205045f3ea17884a1 24
 
< 0.1%
4546caea018ad8c692964e3382debd19 21
 
< 0.1%
c402f431464c72e27330a67f7b94d4fb 20
 
< 0.1%
0f5ac8d5c31de21d2f25e24be15bbffb 18
 
< 0.1%
8d50f5eadf50201ccdcedfb9e2ac8455 16
 
< 0.1%
11f97da02237a49c8e783dfda6f50e8e 15
 
< 0.1%
eae0a83d752b1dd32697e0e7b4221656 15
 
< 0.1%
33176de67c05eeed870fd49f234387a0 15
 
< 0.1%
3e43e6105506432c953e165fb2acf44c 14
 
< 0.1%
Other values (94711) 112190
99.8%

Length

2023-02-10T11:45:54.914559image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
d97b3cfb22b0d6b25ac9ed4e9c2d481b 24
 
< 0.1%
c8460e4251689ba205045f3ea17884a1 24
 
< 0.1%
4546caea018ad8c692964e3382debd19 21
 
< 0.1%
c402f431464c72e27330a67f7b94d4fb 20
 
< 0.1%
0f5ac8d5c31de21d2f25e24be15bbffb 18
 
< 0.1%
8d50f5eadf50201ccdcedfb9e2ac8455 16
 
< 0.1%
11f97da02237a49c8e783dfda6f50e8e 15
 
< 0.1%
eae0a83d752b1dd32697e0e7b4221656 15
 
< 0.1%
33176de67c05eeed870fd49f234387a0 15
 
< 0.1%
31e412b9fb766b6794724ed17a41dfa6 14
 
< 0.1%
Other values (94711) 112190
99.8%

Most occurring characters

ValueCountFrequency (%)
6 225520
 
6.3%
1 225456
 
6.3%
e 225081
 
6.3%
8 224933
 
6.3%
b 224899
 
6.3%
d 224862
 
6.3%
a 224854
 
6.3%
5 224792
 
6.3%
9 224775
 
6.3%
2 224680
 
6.2%
Other values (6) 1346052
37.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2247942
62.5%
Lowercase Letter 1347962
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 225520
10.0%
1 225456
10.0%
8 224933
10.0%
5 224792
10.0%
9 224775
10.0%
2 224680
10.0%
3 224650
10.0%
0 224625
10.0%
7 224466
10.0%
4 224045
10.0%
Lowercase Letter
ValueCountFrequency (%)
e 225081
16.7%
b 224899
16.7%
d 224862
16.7%
a 224854
16.7%
f 224279
16.6%
c 223987
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 2247942
62.5%
Latin 1347962
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
6 225520
10.0%
1 225456
10.0%
8 224933
10.0%
5 224792
10.0%
9 224775
10.0%
2 224680
10.0%
3 224650
10.0%
0 224625
10.0%
7 224466
10.0%
4 224045
10.0%
Latin
ValueCountFrequency (%)
e 225081
16.7%
b 224899
16.7%
d 224862
16.7%
a 224854
16.7%
f 224279
16.6%
c 223987
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3595904
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 225520
 
6.3%
1 225456
 
6.3%
e 225081
 
6.3%
8 224933
 
6.3%
b 224899
 
6.3%
d 224862
 
6.3%
a 224854
 
6.3%
5 224792
 
6.3%
9 224775
 
6.3%
2 224680
 
6.2%
Other values (6) 1346052
37.4%

customer_zip_code_prefix
Real number (ℝ)

Distinct14955
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35131.881
Minimum1003
Maximum99990
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size878.0 KiB
2023-02-10T11:45:55.266948image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1003
5-th percentile3308
Q111250
median24320
Q359063
95-th percentile90630
Maximum99990
Range98987
Interquartile range (IQR)47813

Descriptive statistics

Standard deviation29894.588
Coefficient of variation (CV)0.85092477
Kurtosis-0.79564531
Mean35131.881
Median Absolute Deviation (MAD)16406
Skewness0.77954883
Sum3.9478397 × 109
Variance8.9368637 × 108
MonotonicityNot monotonic
2023-02-10T11:45:55.430517image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22790 151
 
0.1%
22793 150
 
0.1%
24220 145
 
0.1%
24230 137
 
0.1%
22775 124
 
0.1%
35162 112
 
0.1%
11740 106
 
0.1%
29101 106
 
0.1%
13212 105
 
0.1%
13087 104
 
0.1%
Other values (14945) 111132
98.9%
ValueCountFrequency (%)
1003 1
 
< 0.1%
1004 2
 
< 0.1%
1005 6
< 0.1%
1006 2
 
< 0.1%
1007 4
< 0.1%
1008 3
 
< 0.1%
1009 8
< 0.1%
1011 6
< 0.1%
1012 2
 
< 0.1%
1013 3
 
< 0.1%
ValueCountFrequency (%)
99990 1
 
< 0.1%
99980 3
 
< 0.1%
99970 1
 
< 0.1%
99965 2
 
< 0.1%
99960 1
 
< 0.1%
99955 3
 
< 0.1%
99950 9
< 0.1%
99940 2
 
< 0.1%
99930 5
< 0.1%
99925 1
 
< 0.1%

customer_city
Categorical

Distinct4108
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size878.0 KiB
sao paulo
17794 
rio de janeiro
 
7786
belo horizonte
 
3150
brasilia
 
2402
curitiba
 
1749
Other values (4103)
79491 

Length

Max length32
Median length27
Mean length10.338643
Min length3

Characters and Unicode

Total characters1161774
Distinct characters31
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1063 ?
Unique (%)0.9%

Sample

1st rowboa vista
2nd rowboa vista
3rd rowpasso fundo
4th rowsao joaquim da barra
5th rowsao joaquim da barra

Common Values

ValueCountFrequency (%)
sao paulo 17794
 
15.8%
rio de janeiro 7786
 
6.9%
belo horizonte 3150
 
2.8%
brasilia 2402
 
2.1%
curitiba 1749
 
1.6%
campinas 1642
 
1.5%
porto alegre 1616
 
1.4%
salvador 1393
 
1.2%
guarulhos 1314
 
1.2%
sao bernardo do campo 1067
 
0.9%
Other values (4098) 72459
64.5%

Length

2023-02-10T11:45:55.592185image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sao 23948
 
12.2%
paulo 17873
 
9.1%
de 10911
 
5.5%
rio 9372
 
4.8%
janeiro 7786
 
4.0%
do 4858
 
2.5%
belo 3220
 
1.6%
horizonte 3178
 
1.6%
brasilia 2412
 
1.2%
porto 1918
 
1.0%
Other values (3279) 111504
56.6%

Most occurring characters

ValueCountFrequency (%)
a 191445
16.5%
o 143516
12.4%
i 88922
 
7.7%
r 86281
 
7.4%
84608
 
7.3%
e 75418
 
6.5%
s 71066
 
6.1%
n 51541
 
4.4%
u 50894
 
4.4%
l 50710
 
4.4%
Other values (21) 267373
23.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1076645
92.7%
Space Separator 84608
 
7.3%
Dash Punctuation 269
 
< 0.1%
Other Punctuation 250
 
< 0.1%
Decimal Number 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 191445
17.8%
o 143516
13.3%
i 88922
 
8.3%
r 86281
 
8.0%
e 75418
 
7.0%
s 71066
 
6.6%
n 51541
 
4.8%
u 50894
 
4.7%
l 50710
 
4.7%
p 42279
 
3.9%
Other values (16) 224573
20.9%
Decimal Number
ValueCountFrequency (%)
1 1
50.0%
4 1
50.0%
Space Separator
ValueCountFrequency (%)
84608
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 269
100.0%
Other Punctuation
ValueCountFrequency (%)
' 250
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1076645
92.7%
Common 85129
 
7.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 191445
17.8%
o 143516
13.3%
i 88922
 
8.3%
r 86281
 
8.0%
e 75418
 
7.0%
s 71066
 
6.6%
n 51541
 
4.8%
u 50894
 
4.7%
l 50710
 
4.7%
p 42279
 
3.9%
Other values (16) 224573
20.9%
Common
ValueCountFrequency (%)
84608
99.4%
- 269
 
0.3%
' 250
 
0.3%
1 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1161774
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 191445
16.5%
o 143516
12.4%
i 88922
 
7.7%
r 86281
 
7.4%
84608
 
7.3%
e 75418
 
6.5%
s 71066
 
6.1%
n 51541
 
4.4%
u 50894
 
4.4%
l 50710
 
4.4%
Other values (21) 267373
23.0%

customer_state
Categorical

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size878.0 KiB
SP
47399 
RJ
14468 
MG
13110 
RS
6265 
PR
5736 
Other values (22)
25394 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters224744
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRR
2nd rowRR
3rd rowRS
4th rowSP
5th rowSP

Common Values

ValueCountFrequency (%)
SP 47399
42.2%
RJ 14468
 
12.9%
MG 13110
 
11.7%
RS 6265
 
5.6%
PR 5736
 
5.1%
SC 4156
 
3.7%
BA 3766
 
3.4%
DF 2416
 
2.2%
GO 2316
 
2.1%
ES 2237
 
2.0%
Other values (17) 10503
 
9.3%

Length

2023-02-10T11:45:55.745364image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sp 47399
42.2%
rj 14468
 
12.9%
mg 13110
 
11.7%
rs 6265
 
5.6%
pr 5736
 
5.1%
sc 4156
 
3.7%
ba 3766
 
3.4%
df 2416
 
2.2%
go 2316
 
2.1%
es 2237
 
2.0%
Other values (17) 10503
 
9.3%

Most occurring characters

ValueCountFrequency (%)
S 61269
27.3%
P 57213
25.5%
R 27377
12.2%
M 15973
 
7.1%
G 15426
 
6.9%
J 14468
 
6.4%
A 6438
 
2.9%
E 5888
 
2.6%
C 5720
 
2.5%
B 4362
 
1.9%
Other values (7) 10610
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 224744
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 61269
27.3%
P 57213
25.5%
R 27377
12.2%
M 15973
 
7.1%
G 15426
 
6.9%
J 14468
 
6.4%
A 6438
 
2.9%
E 5888
 
2.6%
C 5720
 
2.5%
B 4362
 
1.9%
Other values (7) 10610
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 224744
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 61269
27.3%
P 57213
25.5%
R 27377
12.2%
M 15973
 
7.1%
G 15426
 
6.9%
J 14468
 
6.4%
A 6438
 
2.9%
E 5888
 
2.6%
C 5720
 
2.5%
B 4362
 
1.9%
Other values (7) 10610
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 224744
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 61269
27.3%
P 57213
25.5%
R 27377
12.2%
M 15973
 
7.1%
G 15426
 
6.9%
J 14468
 
6.4%
A 6438
 
2.9%
E 5888
 
2.6%
C 5720
 
2.5%
B 4362
 
1.9%
Other values (7) 10610
 
4.7%

order_id
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct97917
Distinct (%)87.1%
Missing0
Missing (%)0.0%
Memory size878.0 KiB
5a3b1c29a49756e75f1ef513383c0c12
 
22
8272b63d03f5f79c56e9e4120aec44ef
 
21
1b15974a0141d54e36626dca3fdc731a
 
20
428a2f660dc84138d969ccd69a0ab6d5
 
15
9ef13efd6949e4573a18964dd1bbe7f5
 
15
Other values (97912)
112279 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3595904
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique87758 ?
Unique (%)78.1%

Sample

1st row2e7a8482f6fb09756ca50c10d7bfc047
2nd row2e7a8482f6fb09756ca50c10d7bfc047
3rd rowe5fa5a7210941f7d56d0208e4e071d35
4th rowbfbd0f9bdef84302105ad712db648a6c
5th rowbfbd0f9bdef84302105ad712db648a6c

Common Values

ValueCountFrequency (%)
5a3b1c29a49756e75f1ef513383c0c12 22
 
< 0.1%
8272b63d03f5f79c56e9e4120aec44ef 21
 
< 0.1%
1b15974a0141d54e36626dca3fdc731a 20
 
< 0.1%
428a2f660dc84138d969ccd69a0ab6d5 15
 
< 0.1%
9ef13efd6949e4573a18964dd1bbe7f5 15
 
< 0.1%
9bdc4d4c71aa1de4606060929dee888c 14
 
< 0.1%
73c8ab38f07dc94389065f7eba4f297a 14
 
< 0.1%
37ee401157a3a0b28c9c6d0ed8c3b24b 13
 
< 0.1%
2c2a19b5703863c908512d135aa6accc 12
 
< 0.1%
c05d6a79e55da72ca780ce90364abed9 12
 
< 0.1%
Other values (97907) 112214
99.9%

Length

2023-02-10T11:45:55.917222image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5a3b1c29a49756e75f1ef513383c0c12 22
 
< 0.1%
8272b63d03f5f79c56e9e4120aec44ef 21
 
< 0.1%
1b15974a0141d54e36626dca3fdc731a 20
 
< 0.1%
428a2f660dc84138d969ccd69a0ab6d5 15
 
< 0.1%
9ef13efd6949e4573a18964dd1bbe7f5 15
 
< 0.1%
9bdc4d4c71aa1de4606060929dee888c 14
 
< 0.1%
73c8ab38f07dc94389065f7eba4f297a 14
 
< 0.1%
37ee401157a3a0b28c9c6d0ed8c3b24b 13
 
< 0.1%
af822dacd6f5cff7376413c03a388bb7 12
 
< 0.1%
637617b3ffe9e2f7a2411243829226d0 12
 
< 0.1%
Other values (97907) 112214
99.9%

Most occurring characters

ValueCountFrequency (%)
4 225874
 
6.3%
6 225416
 
6.3%
e 225362
 
6.3%
b 225319
 
6.3%
7 225255
 
6.3%
3 225141
 
6.3%
a 224944
 
6.3%
2 224842
 
6.3%
8 224819
 
6.3%
1 224818
 
6.3%
Other values (6) 1344114
37.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2247355
62.5%
Lowercase Letter 1348549
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 225874
10.1%
6 225416
10.0%
7 225255
10.0%
3 225141
10.0%
2 224842
10.0%
8 224819
10.0%
1 224818
10.0%
9 224121
10.0%
0 223894
10.0%
5 223175
9.9%
Lowercase Letter
ValueCountFrequency (%)
e 225362
16.7%
b 225319
16.7%
a 224944
16.7%
c 224756
16.7%
f 224517
16.6%
d 223651
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 2247355
62.5%
Latin 1348549
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
4 225874
10.1%
6 225416
10.0%
7 225255
10.0%
3 225141
10.0%
2 224842
10.0%
8 224819
10.0%
1 224818
10.0%
9 224121
10.0%
0 223894
10.0%
5 223175
9.9%
Latin
ValueCountFrequency (%)
e 225362
16.7%
b 225319
16.7%
a 224944
16.7%
c 224756
16.7%
f 224517
16.6%
d 223651
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3595904
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 225874
 
6.3%
6 225416
 
6.3%
e 225362
 
6.3%
b 225319
 
6.3%
7 225255
 
6.3%
3 225141
 
6.3%
a 224944
 
6.3%
2 224842
 
6.3%
8 224819
 
6.3%
1 224818
 
6.3%
Other values (6) 1344114
37.4%

order_status
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size878.0 KiB
delivered
110013 
shipped
 
1110
canceled
 
529
invoiced
 
358
processing
 
352
Other values (2)
 
10

Length

Max length11
Median length9
Mean length8.9755811
Min length7

Characters and Unicode

Total characters1008604
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowshipped
2nd rowshipped
3rd rowcanceled
4th rowdelivered
5th rowdelivered

Common Values

ValueCountFrequency (%)
delivered 110013
97.9%
shipped 1110
 
1.0%
canceled 529
 
0.5%
invoiced 358
 
0.3%
processing 352
 
0.3%
unavailable 7
 
< 0.1%
approved 3
 
< 0.1%

Length

2023-02-10T11:45:56.087982image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-10T11:45:56.370817image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
delivered 110013
97.9%
shipped 1110
 
1.0%
canceled 529
 
0.5%
invoiced 358
 
0.3%
processing 352
 
0.3%
unavailable 7
 
< 0.1%
approved 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 332927
33.0%
d 222026
22.0%
i 112198
 
11.1%
l 110556
 
11.0%
v 110381
 
10.9%
r 110368
 
10.9%
p 2578
 
0.3%
s 1814
 
0.2%
c 1768
 
0.2%
n 1246
 
0.1%
Other values (6) 2742
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1008604
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 332927
33.0%
d 222026
22.0%
i 112198
 
11.1%
l 110556
 
11.0%
v 110381
 
10.9%
r 110368
 
10.9%
p 2578
 
0.3%
s 1814
 
0.2%
c 1768
 
0.2%
n 1246
 
0.1%
Other values (6) 2742
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 1008604
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 332927
33.0%
d 222026
22.0%
i 112198
 
11.1%
l 110556
 
11.0%
v 110381
 
10.9%
r 110368
 
10.9%
p 2578
 
0.3%
s 1814
 
0.2%
c 1768
 
0.2%
n 1246
 
0.1%
Other values (6) 2742
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1008604
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 332927
33.0%
d 222026
22.0%
i 112198
 
11.1%
l 110556
 
11.0%
v 110381
 
10.9%
r 110368
 
10.9%
p 2578
 
0.3%
s 1814
 
0.2%
c 1768
 
0.2%
n 1246
 
0.1%
Other values (6) 2742
 
0.3%

order_purchase_timestamp
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct97371
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Memory size878.0 KiB
2017-10-17 13:06:29
 
22
2017-07-16 18:19:25
 
21
2018-02-22 15:30:41
 
20
2017-01-30 21:44:49
 
15
2017-11-23 20:30:52
 
15
Other values (97366)
112279 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters2135068
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique86925 ?
Unique (%)77.4%

Sample

1st row2016-09-04 21:15:19
2nd row2016-09-04 21:15:19
3rd row2016-09-05 00:15:34
4th row2016-09-15 12:16:38
5th row2016-09-15 12:16:38

Common Values

ValueCountFrequency (%)
2017-10-17 13:06:29 22
 
< 0.1%
2017-07-16 18:19:25 21
 
< 0.1%
2018-02-22 15:30:41 20
 
< 0.1%
2017-01-30 21:44:49 15
 
< 0.1%
2017-11-23 20:30:52 15
 
< 0.1%
2017-12-13 14:21:15 14
 
< 0.1%
2018-02-21 11:45:07 14
 
< 0.1%
2018-04-12 11:02:51 13
 
< 0.1%
2017-10-09 20:45:45 12
 
< 0.1%
2018-01-12 16:19:31 12
 
< 0.1%
Other values (97361) 112214
99.9%

Length

2023-02-10T11:45:56.567544image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-11-24 1366
 
0.6%
2017-11-25 580
 
0.3%
2017-11-27 480
 
0.2%
2017-11-26 452
 
0.2%
2018-08-06 430
 
0.2%
2018-08-07 429
 
0.2%
2017-11-28 428
 
0.2%
2018-05-15 420
 
0.2%
2018-05-07 417
 
0.2%
2018-05-14 412
 
0.2%
Other values (51075) 219330
97.6%

Most occurring characters

ValueCountFrequency (%)
1 347765
16.3%
0 345779
16.2%
2 273979
12.8%
- 224744
10.5%
: 224744
10.5%
8 117176
 
5.5%
112372
 
5.3%
7 104069
 
4.9%
3 99198
 
4.6%
5 90833
 
4.3%
Other values (3) 194409
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1573208
73.7%
Dash Punctuation 224744
 
10.5%
Other Punctuation 224744
 
10.5%
Space Separator 112372
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 347765
22.1%
0 345779
22.0%
2 273979
17.4%
8 117176
 
7.4%
7 104069
 
6.6%
3 99198
 
6.3%
5 90833
 
5.8%
4 90807
 
5.8%
6 53371
 
3.4%
9 50231
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
- 224744
100.0%
Other Punctuation
ValueCountFrequency (%)
: 224744
100.0%
Space Separator
ValueCountFrequency (%)
112372
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2135068
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 347765
16.3%
0 345779
16.2%
2 273979
12.8%
- 224744
10.5%
: 224744
10.5%
8 117176
 
5.5%
112372
 
5.3%
7 104069
 
4.9%
3 99198
 
4.6%
5 90833
 
4.3%
Other values (3) 194409
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2135068
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 347765
16.3%
0 345779
16.2%
2 273979
12.8%
- 224744
10.5%
: 224744
10.5%
8 117176
 
5.5%
112372
 
5.3%
7 104069
 
4.9%
3 99198
 
4.6%
5 90833
 
4.3%
Other values (3) 194409
9.1%

order_approved_at
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct89534
Distinct (%)79.7%
Missing15
Missing (%)< 0.1%
Memory size878.0 KiB
2018-02-24 03:20:27
 
23
2017-10-18 13:06:21
 
22
2017-07-17 18:25:23
 
21
2018-06-08 19:31:06
 
15
2017-12-15 02:30:41
 
15
Other values (89529)
112261 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters2134783
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique74068 ?
Unique (%)65.9%

Sample

1st row2016-10-07 13:18:03
2nd row2016-10-07 13:18:03
3rd row2016-10-07 13:17:15
4th row2016-09-15 12:16:38
5th row2016-09-15 12:16:38

Common Values

ValueCountFrequency (%)
2018-02-24 03:20:27 23
 
< 0.1%
2017-10-18 13:06:21 22
 
< 0.1%
2017-07-17 18:25:23 21
 
< 0.1%
2018-06-08 19:31:06 15
 
< 0.1%
2017-12-15 02:30:41 15
 
< 0.1%
2017-01-30 22:33:45 15
 
< 0.1%
2017-11-24 10:31:10 15
 
< 0.1%
2018-02-22 11:48:42 14
 
< 0.1%
2018-04-19 22:11:43 13
 
< 0.1%
2018-04-14 02:31:43 13
 
< 0.1%
Other values (89524) 112191
99.8%
(Missing) 15
 
< 0.1%

Length

2023-02-10T11:45:56.687078image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-04-24 1115
 
0.5%
2017-11-24 951
 
0.4%
2017-11-25 875
 
0.4%
2018-07-05 800
 
0.4%
2017-11-28 582
 
0.3%
2018-08-07 493
 
0.2%
2018-05-08 487
 
0.2%
2017-12-05 469
 
0.2%
2018-08-20 466
 
0.2%
2018-05-15 453
 
0.2%
Other values (42032) 218023
97.0%

Most occurring characters

ValueCountFrequency (%)
0 360517
16.9%
1 345025
16.2%
2 273152
12.8%
- 224714
10.5%
: 224714
10.5%
112357
 
5.3%
8 111209
 
5.2%
5 108307
 
5.1%
3 105504
 
4.9%
7 99292
 
4.7%
Other values (3) 169992
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1572998
73.7%
Dash Punctuation 224714
 
10.5%
Other Punctuation 224714
 
10.5%
Space Separator 112357
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 360517
22.9%
1 345025
21.9%
2 273152
17.4%
8 111209
 
7.1%
5 108307
 
6.9%
3 105504
 
6.7%
7 99292
 
6.3%
4 78126
 
5.0%
6 48467
 
3.1%
9 43399
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
- 224714
100.0%
Other Punctuation
ValueCountFrequency (%)
: 224714
100.0%
Space Separator
ValueCountFrequency (%)
112357
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2134783
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 360517
16.9%
1 345025
16.2%
2 273152
12.8%
- 224714
10.5%
: 224714
10.5%
112357
 
5.3%
8 111209
 
5.2%
5 108307
 
5.1%
3 105504
 
4.9%
7 99292
 
4.7%
Other values (3) 169992
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2134783
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 360517
16.9%
1 345025
16.2%
2 273152
12.8%
- 224714
10.5%
: 224714
10.5%
112357
 
5.3%
8 111209
 
5.2%
5 108307
 
5.1%
3 105504
 
4.9%
7 99292
 
4.7%
Other values (3) 169992
8.0%

order_delivered_carrier_date
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct80450
Distinct (%)72.4%
Missing1184
Missing (%)1.1%
Memory size878.0 KiB
2018-05-09 15:48:00
 
48
2018-05-10 18:29:00
 
36
2017-10-20 19:09:07
 
22
2018-05-07 12:31:00
 
21
2018-08-08 15:01:00
 
21
Other values (80445)
111040 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters2112572
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62941 ?
Unique (%)56.6%

Sample

1st row2016-10-18 13:14:51
2nd row2016-10-18 13:14:51
3rd row2016-11-07 17:11:53
4th row2016-11-07 17:11:53
5th row2016-11-07 17:11:53

Common Values

ValueCountFrequency (%)
2018-05-09 15:48:00 48
 
< 0.1%
2018-05-10 18:29:00 36
 
< 0.1%
2017-10-20 19:09:07 22
 
< 0.1%
2018-05-07 12:31:00 21
 
< 0.1%
2018-08-08 15:01:00 21
 
< 0.1%
2017-07-20 15:45:53 21
 
< 0.1%
2018-03-02 00:18:01 20
 
< 0.1%
2018-06-08 14:40:00 19
 
< 0.1%
2018-05-10 14:28:00 18
 
< 0.1%
2018-05-04 15:46:00 18
 
< 0.1%
Other values (80440) 110944
98.7%
(Missing) 1184
 
1.1%

Length

2023-02-10T11:45:56.804927image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-11-28 878
 
0.4%
2017-11-27 762
 
0.3%
2017-11-29 675
 
0.3%
2018-02-27 612
 
0.3%
2018-03-27 588
 
0.3%
2018-08-06 579
 
0.3%
2017-11-30 551
 
0.2%
2018-08-13 531
 
0.2%
2018-05-14 530
 
0.2%
2017-12-06 515
 
0.2%
Other values (37386) 216155
97.2%

Most occurring characters

ValueCountFrequency (%)
0 385503
18.2%
1 329214
15.6%
2 262019
12.4%
- 222376
10.5%
: 222376
10.5%
8 117650
 
5.6%
111188
 
5.3%
7 100960
 
4.8%
3 93472
 
4.4%
4 87465
 
4.1%
Other values (3) 180349
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1556632
73.7%
Dash Punctuation 222376
 
10.5%
Other Punctuation 222376
 
10.5%
Space Separator 111188
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 385503
24.8%
1 329214
21.1%
2 262019
16.8%
8 117650
 
7.6%
7 100960
 
6.5%
3 93472
 
6.0%
4 87465
 
5.6%
5 85177
 
5.5%
6 48927
 
3.1%
9 46245
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
- 222376
100.0%
Other Punctuation
ValueCountFrequency (%)
: 222376
100.0%
Space Separator
ValueCountFrequency (%)
111188
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2112572
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 385503
18.2%
1 329214
15.6%
2 262019
12.4%
- 222376
10.5%
: 222376
10.5%
8 117650
 
5.6%
111188
 
5.3%
7 100960
 
4.8%
3 93472
 
4.4%
4 87465
 
4.1%
Other values (3) 180349
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2112572
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 385503
18.2%
1 329214
15.6%
2 262019
12.4%
- 222376
10.5%
: 222376
10.5%
8 117650
 
5.6%
111188
 
5.3%
7 100960
 
4.8%
3 93472
 
4.4%
4 87465
 
4.1%
Other values (3) 180349
8.5%

order_delivered_customer_date
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct95022
Distinct (%)86.4%
Missing2360
Missing (%)2.1%
Memory size878.0 KiB
2017-10-22 14:43:54
 
22
2017-07-31 18:03:02
 
21
2018-03-05 15:22:27
 
20
2017-12-13 20:19:35
 
15
2017-02-14 10:48:10
 
15
Other values (95017)
109919 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters2090228
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique84401 ?
Unique (%)76.7%

Sample

1st row2016-11-09 07:47:38
2nd row2016-11-09 07:47:38
3rd row2016-11-09 07:47:38
4th row2016-10-31 11:07:42
5th row2016-10-26 14:02:13

Common Values

ValueCountFrequency (%)
2017-10-22 14:43:54 22
 
< 0.1%
2017-07-31 18:03:02 21
 
< 0.1%
2018-03-05 15:22:27 20
 
< 0.1%
2017-12-13 20:19:35 15
 
< 0.1%
2017-02-14 10:48:10 15
 
< 0.1%
2018-03-01 20:47:01 14
 
< 0.1%
2017-12-28 09:05:34 14
 
< 0.1%
2018-04-23 17:47:44 13
 
< 0.1%
2018-06-18 21:04:38 12
 
< 0.1%
2018-05-15 19:37:06 12
 
< 0.1%
Other values (95012) 109854
97.8%
(Missing) 2360
 
2.1%

Length

2023-02-10T11:45:56.924098image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-05-14 512
 
0.2%
2018-08-13 498
 
0.2%
2018-05-21 496
 
0.2%
2018-08-27 494
 
0.2%
2018-05-18 488
 
0.2%
2017-12-11 476
 
0.2%
2018-04-11 470
 
0.2%
2018-05-03 463
 
0.2%
2017-06-19 460
 
0.2%
2018-07-30 454
 
0.2%
Other values (41602) 215213
97.8%

Most occurring characters

ValueCountFrequency (%)
1 323126
15.5%
0 320225
15.3%
2 277020
13.3%
- 220024
10.5%
: 220024
10.5%
8 129261
6.2%
110012
 
5.3%
3 101890
 
4.9%
7 101623
 
4.9%
4 95107
 
4.6%
Other values (3) 191916
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1540168
73.7%
Dash Punctuation 220024
 
10.5%
Other Punctuation 220024
 
10.5%
Space Separator 110012
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 323126
21.0%
0 320225
20.8%
2 277020
18.0%
8 129261
8.4%
3 101890
 
6.6%
7 101623
 
6.6%
4 95107
 
6.2%
5 89012
 
5.8%
6 55431
 
3.6%
9 47473
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 220024
100.0%
Other Punctuation
ValueCountFrequency (%)
: 220024
100.0%
Space Separator
ValueCountFrequency (%)
110012
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2090228
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 323126
15.5%
0 320225
15.3%
2 277020
13.3%
- 220024
10.5%
: 220024
10.5%
8 129261
6.2%
110012
 
5.3%
3 101890
 
4.9%
7 101623
 
4.9%
4 95107
 
4.6%
Other values (3) 191916
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2090228
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 323126
15.5%
0 320225
15.3%
2 277020
13.3%
- 220024
10.5%
: 220024
10.5%
8 129261
6.2%
110012
 
5.3%
3 101890
 
4.9%
7 101623
 
4.9%
4 95107
 
4.6%
Other values (3) 191916
9.2%
Distinct450
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size878.0 KiB
2017-12-20 00:00:00
 
607
2018-03-12 00:00:00
 
593
2018-05-29 00:00:00
 
591
2018-03-13 00:00:00
 
589
2018-07-05 00:00:00
 
571
Other values (445)
109421 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters2135068
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)< 0.1%

Sample

1st row2016-10-20 00:00:00
2nd row2016-10-20 00:00:00
3rd row2016-10-28 00:00:00
4th row2016-10-04 00:00:00
5th row2016-10-04 00:00:00

Common Values

ValueCountFrequency (%)
2017-12-20 00:00:00 607
 
0.5%
2018-03-12 00:00:00 593
 
0.5%
2018-05-29 00:00:00 591
 
0.5%
2018-03-13 00:00:00 589
 
0.5%
2018-07-05 00:00:00 571
 
0.5%
2017-12-18 00:00:00 565
 
0.5%
2017-12-19 00:00:00 565
 
0.5%
2018-05-28 00:00:00 563
 
0.5%
2018-02-14 00:00:00 558
 
0.5%
2018-05-30 00:00:00 557
 
0.5%
Other values (440) 106613
94.9%

Length

2023-02-10T11:45:57.084947image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00 112372
50.0%
2017-12-20 607
 
0.3%
2018-03-12 593
 
0.3%
2018-05-29 591
 
0.3%
2018-03-13 589
 
0.3%
2018-07-05 571
 
0.3%
2017-12-18 565
 
0.3%
2017-12-19 565
 
0.3%
2018-05-28 563
 
0.3%
2018-02-14 558
 
0.2%
Other values (441) 107170
47.7%

Most occurring characters

ValueCountFrequency (%)
0 929457
43.5%
- 224744
 
10.5%
: 224744
 
10.5%
1 192328
 
9.0%
2 175291
 
8.2%
112372
 
5.3%
8 93359
 
4.4%
7 68148
 
3.2%
3 30055
 
1.4%
5 23107
 
1.1%
Other values (3) 61463
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1573208
73.7%
Dash Punctuation 224744
 
10.5%
Other Punctuation 224744
 
10.5%
Space Separator 112372
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 929457
59.1%
1 192328
 
12.2%
2 175291
 
11.1%
8 93359
 
5.9%
7 68148
 
4.3%
3 30055
 
1.9%
5 23107
 
1.5%
6 21757
 
1.4%
4 21124
 
1.3%
9 18582
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
- 224744
100.0%
Other Punctuation
ValueCountFrequency (%)
: 224744
100.0%
Space Separator
ValueCountFrequency (%)
112372
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2135068
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 929457
43.5%
- 224744
 
10.5%
: 224744
 
10.5%
1 192328
 
9.0%
2 175291
 
8.2%
112372
 
5.3%
8 93359
 
4.4%
7 68148
 
3.2%
3 30055
 
1.4%
5 23107
 
1.1%
Other values (3) 61463
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2135068
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 929457
43.5%
- 224744
 
10.5%
: 224744
 
10.5%
1 192328
 
9.0%
2 175291
 
8.2%
112372
 
5.3%
8 93359
 
4.4%
7 68148
 
3.2%
3 30055
 
1.4%
5 23107
 
1.1%
Other values (3) 61463
 
2.9%

order_item_id
Real number (ℝ)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1960097
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size878.0 KiB
2023-02-10T11:45:57.225749image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum21
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.69124255
Coefficient of variation (CV)0.57795732
Kurtosis92.983464
Mean1.1960097
Median Absolute Deviation (MAD)0
Skewness7.1912707
Sum134398
Variance0.47781626
MonotonicityNot monotonic
2023-02-10T11:45:57.425261image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 98465
87.6%
2 9766
 
8.7%
3 2270
 
2.0%
4 959
 
0.9%
5 456
 
0.4%
6 252
 
0.2%
7 58
 
0.1%
8 36
 
< 0.1%
9 28
 
< 0.1%
10 25
 
< 0.1%
Other values (11) 57
 
0.1%
ValueCountFrequency (%)
1 98465
87.6%
2 9766
 
8.7%
3 2270
 
2.0%
4 959
 
0.9%
5 456
 
0.4%
6 252
 
0.2%
7 58
 
0.1%
8 36
 
< 0.1%
9 28
 
< 0.1%
10 25
 
< 0.1%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 2
 
< 0.1%
19 2
 
< 0.1%
18 2
 
< 0.1%
17 2
 
< 0.1%
16 2
 
< 0.1%
15 4
 
< 0.1%
14 6
< 0.1%
13 7
< 0.1%
12 12
< 0.1%

product_id
Categorical

Distinct32789
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Memory size878.0 KiB
aca2eb7d00ea1a7b8ebd4e68314663af
 
524
422879e10f46682990de24d770e7f83d
 
486
99a4788cb24856965c36a24e339b6058
 
482
389d119b48cf3043d311335e499d9c6b
 
391
368c6c730842d78016ad823897a372db
 
388
Other values (32784)
110101 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3595904
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17949 ?
Unique (%)16.0%

Sample

1st rowf293394c72c9b5fafd7023301fc21fc2
2nd rowc1488892604e4ba5cff5b4eb4d595400
3rd rowf3c2d01a84c947b078e32bbef0718962
4th row5a6b04657a4c5ee34285d1e4619a96b4
5th row5a6b04657a4c5ee34285d1e4619a96b4

Common Values

ValueCountFrequency (%)
aca2eb7d00ea1a7b8ebd4e68314663af 524
 
0.5%
422879e10f46682990de24d770e7f83d 486
 
0.4%
99a4788cb24856965c36a24e339b6058 482
 
0.4%
389d119b48cf3043d311335e499d9c6b 391
 
0.3%
368c6c730842d78016ad823897a372db 388
 
0.3%
53759a2ecddad2bb87a079a1f1519f73 373
 
0.3%
d1c427060a0f73f6b889a5c7c61f2ac4 340
 
0.3%
53b36df67ebb7c41585e8d54d6772e08 320
 
0.3%
154e7e31ebfa092203795c972e5804a6 292
 
0.3%
3dd2a17168ec895c781a9191c1e95ad7 272
 
0.2%
Other values (32779) 108504
96.6%

Length

2023-02-10T11:45:57.690572image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
aca2eb7d00ea1a7b8ebd4e68314663af 524
 
0.5%
422879e10f46682990de24d770e7f83d 486
 
0.4%
99a4788cb24856965c36a24e339b6058 482
 
0.4%
389d119b48cf3043d311335e499d9c6b 391
 
0.3%
368c6c730842d78016ad823897a372db 388
 
0.3%
53759a2ecddad2bb87a079a1f1519f73 373
 
0.3%
d1c427060a0f73f6b889a5c7c61f2ac4 340
 
0.3%
53b36df67ebb7c41585e8d54d6772e08 320
 
0.3%
154e7e31ebfa092203795c972e5804a6 292
 
0.3%
3dd2a17168ec895c781a9191c1e95ad7 272
 
0.2%
Other values (32779) 108504
96.6%

Most occurring characters

ValueCountFrequency (%)
3 231245
 
6.4%
9 228876
 
6.4%
e 226947
 
6.3%
7 226446
 
6.3%
8 226313
 
6.3%
4 225802
 
6.3%
a 225307
 
6.3%
c 224539
 
6.2%
0 224455
 
6.2%
2 224352
 
6.2%
Other values (6) 1331622
37.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2257039
62.8%
Lowercase Letter 1338865
37.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 231245
10.2%
9 228876
10.1%
7 226446
10.0%
8 226313
10.0%
4 225802
10.0%
0 224455
9.9%
2 224352
9.9%
6 223663
9.9%
5 223639
9.9%
1 222248
9.8%
Lowercase Letter
ValueCountFrequency (%)
e 226947
17.0%
a 225307
16.8%
c 224539
16.8%
b 223178
16.7%
d 221055
16.5%
f 217839
16.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2257039
62.8%
Latin 1338865
37.2%

Most frequent character per script

Common
ValueCountFrequency (%)
3 231245
10.2%
9 228876
10.1%
7 226446
10.0%
8 226313
10.0%
4 225802
10.0%
0 224455
9.9%
2 224352
9.9%
6 223663
9.9%
5 223639
9.9%
1 222248
9.8%
Latin
ValueCountFrequency (%)
e 226947
17.0%
a 225307
16.8%
c 224539
16.8%
b 223178
16.7%
d 221055
16.5%
f 217839
16.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3595904
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 231245
 
6.4%
9 228876
 
6.4%
e 226947
 
6.3%
7 226446
 
6.3%
8 226313
 
6.3%
4 225802
 
6.3%
a 225307
 
6.3%
c 224539
 
6.2%
0 224455
 
6.2%
2 224352
 
6.2%
Other values (6) 1331622
37.0%

seller_id
Categorical

Distinct3090
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size878.0 KiB
6560211a19b47992c3666cc44a7e94c0
 
2020
4a3ca9315b744ce9f8e9374361493884
 
1984
1f50f920176fa81dab994f9023523100
 
1932
cc419e0650a3c5ba77189a1882b7556a
 
1811
da8622b14eb17ae2831f4ac5b9dab84a
 
1568
Other values (3085)
103057 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3595904
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique504 ?
Unique (%)0.4%

Sample

1st row1554a68530182680ad5c8b042c3ab563
2nd row1554a68530182680ad5c8b042c3ab563
3rd rowa425f92c199eb576938df686728acd20
4th rowecccfa2bb93b34a3bf033cc5d1dcdc69
5th rowecccfa2bb93b34a3bf033cc5d1dcdc69

Common Values

ValueCountFrequency (%)
6560211a19b47992c3666cc44a7e94c0 2020
 
1.8%
4a3ca9315b744ce9f8e9374361493884 1984
 
1.8%
1f50f920176fa81dab994f9023523100 1932
 
1.7%
cc419e0650a3c5ba77189a1882b7556a 1811
 
1.6%
da8622b14eb17ae2831f4ac5b9dab84a 1568
 
1.4%
955fee9216a65b617aa5c0531780ce60 1489
 
1.3%
1025f0e2d44d7041d6cf58b6550e0bfa 1431
 
1.3%
7c67e1448b00f6e969d365cea6b010ab 1367
 
1.2%
ea8482cd71df3c1969d7b9473ff13abc 1197
 
1.1%
7a67c85e85bb2ce8582c35f2203ad736 1166
 
1.0%
Other values (3080) 96407
85.8%

Length

2023-02-10T11:45:57.886838image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
6560211a19b47992c3666cc44a7e94c0 2020
 
1.8%
4a3ca9315b744ce9f8e9374361493884 1984
 
1.8%
1f50f920176fa81dab994f9023523100 1932
 
1.7%
cc419e0650a3c5ba77189a1882b7556a 1811
 
1.6%
da8622b14eb17ae2831f4ac5b9dab84a 1568
 
1.4%
955fee9216a65b617aa5c0531780ce60 1489
 
1.3%
1025f0e2d44d7041d6cf58b6550e0bfa 1431
 
1.3%
7c67e1448b00f6e969d365cea6b010ab 1367
 
1.2%
ea8482cd71df3c1969d7b9473ff13abc 1197
 
1.1%
7a67c85e85bb2ce8582c35f2203ad736 1166
 
1.0%
Other values (3080) 96407
85.8%

Most occurring characters

ValueCountFrequency (%)
1 243702
 
6.8%
c 237265
 
6.6%
4 235579
 
6.6%
6 231538
 
6.4%
0 230731
 
6.4%
a 229324
 
6.4%
b 228696
 
6.4%
3 228492
 
6.4%
9 222943
 
6.2%
2 222078
 
6.2%
Other values (6) 1285556
35.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2273806
63.2%
Lowercase Letter 1322098
36.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 243702
10.7%
4 235579
10.4%
6 231538
10.2%
0 230731
10.1%
3 228492
10.0%
9 222943
9.8%
2 222078
9.8%
8 219820
9.7%
5 219763
9.7%
7 219160
9.6%
Lowercase Letter
ValueCountFrequency (%)
c 237265
17.9%
a 229324
17.3%
b 228696
17.3%
e 211716
16.0%
f 208617
15.8%
d 206480
15.6%

Most occurring scripts

ValueCountFrequency (%)
Common 2273806
63.2%
Latin 1322098
36.8%

Most frequent character per script

Common
ValueCountFrequency (%)
1 243702
10.7%
4 235579
10.4%
6 231538
10.2%
0 230731
10.1%
3 228492
10.0%
9 222943
9.8%
2 222078
9.8%
8 219820
9.7%
5 219763
9.7%
7 219160
9.6%
Latin
ValueCountFrequency (%)
c 237265
17.9%
a 229324
17.3%
b 228696
17.3%
e 211716
16.0%
f 208617
15.8%
d 206480
15.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3595904
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 243702
 
6.8%
c 237265
 
6.6%
4 235579
 
6.6%
6 231538
 
6.4%
0 230731
 
6.4%
a 229324
 
6.4%
b 228696
 
6.4%
3 228492
 
6.4%
9 222943
 
6.2%
2 222078
 
6.2%
Other values (6) 1285556
35.8%

shipping_limit_date
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct92643
Distinct (%)82.4%
Missing0
Missing (%)0.0%
Memory size878.0 KiB
2017-10-24 13:06:21
 
22
2017-07-21 18:25:23
 
21
2018-03-01 02:50:48
 
21
2017-02-03 21:44:49
 
15
2017-11-30 10:30:51
 
15
Other values (92638)
112278 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters2135068
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique78886 ?
Unique (%)70.2%

Sample

1st row2016-10-26 18:25:19
2nd row2016-10-26 18:25:19
3rd row2016-09-19 00:15:34
4th row2016-09-19 23:11:33
5th row2016-09-19 23:11:33

Common Values

ValueCountFrequency (%)
2017-10-24 13:06:21 22
 
< 0.1%
2017-07-21 18:25:23 21
 
< 0.1%
2018-03-01 02:50:48 21
 
< 0.1%
2017-02-03 21:44:49 15
 
< 0.1%
2017-11-30 10:30:51 15
 
< 0.1%
2017-12-21 02:30:41 15
 
< 0.1%
2018-02-28 11:48:12 14
 
< 0.1%
2018-06-13 17:30:35 13
 
< 0.1%
2018-04-19 02:30:52 13
 
< 0.1%
2018-04-25 22:11:43 13
 
< 0.1%
Other values (92633) 112210
99.9%

Length

2023-02-10T11:45:58.063077image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-11-30 1645
 
0.7%
2017-12-07 756
 
0.3%
2018-04-19 705
 
0.3%
2018-03-08 662
 
0.3%
2018-05-10 658
 
0.3%
2018-01-18 657
 
0.3%
2018-03-01 652
 
0.3%
2018-08-07 649
 
0.3%
2018-02-22 644
 
0.3%
2018-03-22 627
 
0.3%
Other values (40510) 217089
96.6%

Most occurring characters

ValueCountFrequency (%)
0 360810
16.9%
1 345224
16.2%
2 275312
12.9%
- 224744
10.5%
: 224744
10.5%
8 113258
 
5.3%
112372
 
5.3%
3 108600
 
5.1%
5 106431
 
5.0%
7 96448
 
4.5%
Other values (3) 167125
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1573208
73.7%
Dash Punctuation 224744
 
10.5%
Other Punctuation 224744
 
10.5%
Space Separator 112372
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 360810
22.9%
1 345224
21.9%
2 275312
17.5%
8 113258
 
7.2%
3 108600
 
6.9%
5 106431
 
6.8%
7 96448
 
6.1%
4 75487
 
4.8%
6 47066
 
3.0%
9 44572
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
- 224744
100.0%
Other Punctuation
ValueCountFrequency (%)
: 224744
100.0%
Space Separator
ValueCountFrequency (%)
112372
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2135068
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 360810
16.9%
1 345224
16.2%
2 275312
12.9%
- 224744
10.5%
: 224744
10.5%
8 113258
 
5.3%
112372
 
5.3%
3 108600
 
5.1%
5 106431
 
5.0%
7 96448
 
4.5%
Other values (3) 167125
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2135068
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 360810
16.9%
1 345224
16.2%
2 275312
12.9%
- 224744
10.5%
: 224744
10.5%
8 113258
 
5.3%
112372
 
5.3%
3 108600
 
5.1%
5 106431
 
5.0%
7 96448
 
4.5%
Other values (3) 167125
7.8%

price
Real number (ℝ)

Distinct5948
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.37896
Minimum0.85
Maximum6735
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size878.0 KiB
2023-02-10T11:45:58.234353image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.85
5-th percentile17
Q139.9
median74.9
Q3134.9
95-th percentile349.9
Maximum6735
Range6734.15
Interquartile range (IQR)95

Descriptive statistics

Standard deviation182.15239
Coefficient of variation (CV)1.513158
Kurtosis109.42271
Mean120.37896
Median Absolute Deviation (MAD)42
Skewness7.6696487
Sum13527225
Variance33179.492
MonotonicityNot monotonic
2023-02-10T11:45:58.411656image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.9 2472
 
2.2%
69.9 2000
 
1.8%
49.9 1946
 
1.7%
89.9 1545
 
1.4%
99.9 1429
 
1.3%
39.9 1325
 
1.2%
29.9 1319
 
1.2%
79.9 1209
 
1.1%
19.9 1194
 
1.1%
29.99 1171
 
1.0%
Other values (5938) 96762
86.1%
ValueCountFrequency (%)
0.85 3
 
< 0.1%
1.2 20
< 0.1%
2.2 1
 
< 0.1%
2.29 1
 
< 0.1%
2.9 1
 
< 0.1%
2.99 1
 
< 0.1%
3 2
 
< 0.1%
3.06 3
 
< 0.1%
3.49 3
 
< 0.1%
3.5 7
 
< 0.1%
ValueCountFrequency (%)
6735 1
< 0.1%
6499 1
< 0.1%
4799 1
< 0.1%
4690 1
< 0.1%
4590 1
< 0.1%
4399.87 1
< 0.1%
4099.99 1
< 0.1%
4059 1
< 0.1%
3999.9 1
< 0.1%
3999 1
< 0.1%

freight_value
Real number (ℝ)

Distinct6976
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.977752
Minimum0
Maximum409.68
Zeros382
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size878.0 KiB
2023-02-10T11:45:58.698440image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.78
Q113.07
median16.25
Q321.15
95-th percentile45.12
Maximum409.68
Range409.68
Interquartile range (IQR)8.08

Descriptive statistics

Standard deviation15.781421
Coefficient of variation (CV)0.78994982
Kurtosis60.037405
Mean19.977752
Median Absolute Deviation (MAD)3.6
Skewness5.6452126
Sum2244939.9
Variance249.05326
MonotonicityNot monotonic
2023-02-10T11:45:58.854427image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.1 3700
 
3.3%
7.78 2255
 
2.0%
14.1 1879
 
1.7%
11.85 1861
 
1.7%
18.23 1572
 
1.4%
7.39 1521
 
1.4%
16.11 1159
 
1.0%
15.23 1005
 
0.9%
8.72 923
 
0.8%
16.79 869
 
0.8%
Other values (6966) 95628
85.1%
ValueCountFrequency (%)
0 382
0.3%
0.01 4
 
< 0.1%
0.02 3
 
< 0.1%
0.03 14
 
< 0.1%
0.04 4
 
< 0.1%
0.05 4
 
< 0.1%
0.06 11
 
< 0.1%
0.07 1
 
< 0.1%
0.08 12
 
< 0.1%
0.09 6
 
< 0.1%
ValueCountFrequency (%)
409.68 1
< 0.1%
375.28 2
< 0.1%
339.59 1
< 0.1%
338.3 1
< 0.1%
322.1 1
< 0.1%
321.88 1
< 0.1%
321.46 1
< 0.1%
317.47 1
< 0.1%
314.4 1
< 0.1%
314.02 1
< 0.1%

review_id
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct97709
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Memory size878.0 KiB
e8236fe7b6e1bdd513a500de361e2b87
 
21
be332150a9c96e68c9565ea53cba2355
 
20
2e3a6e4930334530774ac3a6f6b62388
 
15
d638a70f2be180ef55395eabb78fd88c
 
15
03129dea7c12fa5878b2e629ccdf2ce6
 
14
Other values (97704)
112287 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3595904
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique87398 ?
Unique (%)77.8%

Sample

1st rowcef1ee03ded4d6272894a2eead6e1328
2nd rowcef1ee03ded4d6272894a2eead6e1328
3rd rowa93139d9d1314158c080e3db7e79618b
4th row6916ca4502d6d3bfd39818759d55d536
5th row6916ca4502d6d3bfd39818759d55d536

Common Values

ValueCountFrequency (%)
e8236fe7b6e1bdd513a500de361e2b87 21
 
< 0.1%
be332150a9c96e68c9565ea53cba2355 20
 
< 0.1%
2e3a6e4930334530774ac3a6f6b62388 15
 
< 0.1%
d638a70f2be180ef55395eabb78fd88c 15
 
< 0.1%
03129dea7c12fa5878b2e629ccdf2ce6 14
 
< 0.1%
ee4bc8e340e8648a44c2e33fee6b27e4 14
 
< 0.1%
ec530e1eb28f81c279430f63836c4c0c 13
 
< 0.1%
e8f500e8052dd5fac20fee5a8c880367 13
 
< 0.1%
d3f6a183fd58d4afd1b44a1bb410d7c2 12
 
< 0.1%
a057d9706ccaec5be597b8048e23f6c6 12
 
< 0.1%
Other values (97699) 112223
99.9%

Length

2023-02-10T11:45:59.010794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
e8236fe7b6e1bdd513a500de361e2b87 21
 
< 0.1%
be332150a9c96e68c9565ea53cba2355 20
 
< 0.1%
2e3a6e4930334530774ac3a6f6b62388 15
 
< 0.1%
d638a70f2be180ef55395eabb78fd88c 15
 
< 0.1%
03129dea7c12fa5878b2e629ccdf2ce6 14
 
< 0.1%
ee4bc8e340e8648a44c2e33fee6b27e4 14
 
< 0.1%
ec530e1eb28f81c279430f63836c4c0c 13
 
< 0.1%
e8f500e8052dd5fac20fee5a8c880367 13
 
< 0.1%
40b1644940367763775a63267ca6d957 12
 
< 0.1%
f676b4a89abc42681e4cd67dfb2621d5 12
 
< 0.1%
Other values (97699) 112223
99.9%

Most occurring characters

ValueCountFrequency (%)
a 225614
 
6.3%
6 225546
 
6.3%
5 225186
 
6.3%
8 225111
 
6.3%
b 224976
 
6.3%
f 224963
 
6.3%
0 224784
 
6.3%
d 224761
 
6.3%
2 224734
 
6.2%
c 224515
 
6.2%
Other values (6) 1345714
37.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2246606
62.5%
Lowercase Letter 1349298
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 225546
10.0%
5 225186
10.0%
8 225111
10.0%
0 224784
10.0%
2 224734
10.0%
9 224417
10.0%
4 224305
10.0%
1 224298
10.0%
7 224240
10.0%
3 223985
10.0%
Lowercase Letter
ValueCountFrequency (%)
a 225614
16.7%
b 224976
16.7%
f 224963
16.7%
d 224761
16.7%
c 224515
16.6%
e 224469
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 2246606
62.5%
Latin 1349298
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
6 225546
10.0%
5 225186
10.0%
8 225111
10.0%
0 224784
10.0%
2 224734
10.0%
9 224417
10.0%
4 224305
10.0%
1 224298
10.0%
7 224240
10.0%
3 223985
10.0%
Latin
ValueCountFrequency (%)
a 225614
16.7%
b 224976
16.7%
f 224963
16.7%
d 224761
16.7%
c 224515
16.6%
e 224469
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3595904
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 225614
 
6.3%
6 225546
 
6.3%
5 225186
 
6.3%
8 225111
 
6.3%
b 224976
 
6.3%
f 224963
 
6.3%
0 224784
 
6.3%
d 224761
 
6.3%
2 224734
 
6.2%
c 224515
 
6.2%
Other values (6) 1345714
37.4%

review_score
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size878.0 KiB
5
63525 
4
21315 
1
14235 
3
9423 
2
 
3874

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters112372
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
5 63525
56.5%
4 21315
 
19.0%
1 14235
 
12.7%
3 9423
 
8.4%
2 3874
 
3.4%

Length

2023-02-10T11:45:59.215379image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-10T11:45:59.359730image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
5 63525
56.5%
4 21315
 
19.0%
1 14235
 
12.7%
3 9423
 
8.4%
2 3874
 
3.4%

Most occurring characters

ValueCountFrequency (%)
5 63525
56.5%
4 21315
 
19.0%
1 14235
 
12.7%
3 9423
 
8.4%
2 3874
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 112372
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 63525
56.5%
4 21315
 
19.0%
1 14235
 
12.7%
3 9423
 
8.4%
2 3874
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common 112372
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 63525
56.5%
4 21315
 
19.0%
1 14235
 
12.7%
3 9423
 
8.4%
2 3874
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 112372
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 63525
56.5%
4 21315
 
19.0%
1 14235
 
12.7%
3 9423
 
8.4%
2 3874
 
3.4%

review_comment_title
Categorical

HIGH CARDINALITY  MISSING 

Distinct4497
Distinct (%)33.5%
Missing98938
Missing (%)88.0%
Memory size878.0 KiB
Recomendo
 
471
recomendo
 
392
Bom
 
322
super recomendo
 
307
Excelente
 
281
Other values (4492)
11661 

Length

Max length26
Median length20
Mean length12.172994
Min length1

Characters and Unicode

Total characters163532
Distinct characters125
Distinct categories14 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3167 ?
Unique (%)23.6%

Sample

1st rowsuper recomendado
2nd rowotimo
3rd rowMuito bom.
4th rowA câmera não funcionou
5th rowCadê meu produto? Acabou!

Common Values

ValueCountFrequency (%)
Recomendo 471
 
0.4%
recomendo 392
 
0.3%
Bom 322
 
0.3%
super recomendo 307
 
0.3%
Excelente 281
 
0.3%
Muito bom 268
 
0.2%
Ótimo 265
 
0.2%
Super recomendo 251
 
0.2%
Ótimo 231
 
0.2%
Otimo 198
 
0.2%
Other values (4487) 10448
 
9.3%
(Missing) 98938
88.0%

Length

2023-02-10T11:45:59.498982image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
recomendo 2389
 
9.3%
produto 1481
 
5.8%
bom 1462
 
5.7%
super 1019
 
4.0%
muito 1006
 
3.9%
não 878
 
3.4%
ótimo 794
 
3.1%
excelente 739
 
2.9%
entrega 674
 
2.6%
recebi 420
 
1.6%
Other values (2083) 14758
57.6%

Most occurring characters

ValueCountFrequency (%)
o 20356
 
12.4%
e 17478
 
10.7%
14516
 
8.9%
r 9423
 
5.8%
t 9043
 
5.5%
a 8760
 
5.4%
m 8103
 
5.0%
d 7864
 
4.8%
i 7763
 
4.7%
n 7343
 
4.5%
Other values (115) 52883
32.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 127012
77.7%
Uppercase Letter 18080
 
11.1%
Space Separator 14516
 
8.9%
Other Punctuation 2600
 
1.6%
Decimal Number 1224
 
0.7%
Other Symbol 47
 
< 0.1%
Dash Punctuation 19
 
< 0.1%
Modifier Symbol 13
 
< 0.1%
Math Symbol 8
 
< 0.1%
Close Punctuation 6
 
< 0.1%
Other values (4) 7
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 20356
16.0%
e 17478
13.8%
r 9423
 
7.4%
t 9043
 
7.1%
a 8760
 
6.9%
m 8103
 
6.4%
d 7864
 
6.2%
i 7763
 
6.1%
n 7343
 
5.8%
c 5688
 
4.5%
Other values (31) 25191
19.8%
Uppercase Letter
ValueCountFrequency (%)
E 2415
13.4%
R 1947
10.8%
O 1608
 
8.9%
P 1529
 
8.5%
M 1397
 
7.7%
N 1138
 
6.3%
S 1012
 
5.6%
A 935
 
5.2%
Ó 915
 
5.1%
B 864
 
4.8%
Other values (26) 4320
23.9%
Other Punctuation
ValueCountFrequency (%)
! 1166
44.8%
. 757
29.1%
* 404
 
15.5%
, 146
 
5.6%
? 49
 
1.9%
/ 39
 
1.5%
% 21
 
0.8%
: 6
 
0.2%
" 3
 
0.1%
; 3
 
0.1%
Other values (4) 6
 
0.2%
Decimal Number
ValueCountFrequency (%)
0 460
37.6%
1 409
33.4%
5 81
 
6.6%
2 77
 
6.3%
8 46
 
3.8%
3 40
 
3.3%
4 37
 
3.0%
9 29
 
2.4%
7 24
 
2.0%
6 21
 
1.7%
Other Symbol
ValueCountFrequency (%)
👍 16
34.0%
😍 9
19.1%
👏 6
 
12.8%
🌟 6
 
12.8%
💥 5
 
10.6%
👎 1
 
2.1%
🔟 1
 
2.1%
🚚 1
 
2.1%
🤗 1
 
2.1%
😀 1
 
2.1%
Modifier Symbol
ValueCountFrequency (%)
´ 6
46.2%
🏻 3
23.1%
🏼 2
 
15.4%
🏽 2
 
15.4%
Math Symbol
ValueCountFrequency (%)
+ 7
87.5%
= 1
 
12.5%
Close Punctuation
ValueCountFrequency (%)
) 5
83.3%
] 1
 
16.7%
Space Separator
ValueCountFrequency (%)
14516
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 1
100.0%
Other Letter
ValueCountFrequency (%)
ª 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 145093
88.7%
Common 18439
 
11.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 20356
14.0%
e 17478
12.0%
r 9423
 
6.5%
t 9043
 
6.2%
a 8760
 
6.0%
m 8103
 
5.6%
d 7864
 
5.4%
i 7763
 
5.4%
n 7343
 
5.1%
c 5688
 
3.9%
Other values (68) 43272
29.8%
Common
ValueCountFrequency (%)
14516
78.7%
! 1166
 
6.3%
. 757
 
4.1%
0 460
 
2.5%
1 409
 
2.2%
* 404
 
2.2%
, 146
 
0.8%
5 81
 
0.4%
2 77
 
0.4%
? 49
 
0.3%
Other values (37) 374
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 160022
97.9%
None 3500
 
2.1%
Emoticons 10
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 20356
12.7%
e 17478
 
10.9%
14516
 
9.1%
r 9423
 
5.9%
t 9043
 
5.7%
a 8760
 
5.5%
m 8103
 
5.1%
d 7864
 
4.9%
i 7763
 
4.9%
n 7343
 
4.6%
Other values (75) 49373
30.9%
None
ValueCountFrequency (%)
ã 1063
30.4%
Ó 915
26.1%
á 348
 
9.9%
ç 334
 
9.5%
ó 245
 
7.0%
é 243
 
6.9%
à 69
 
2.0%
í 64
 
1.8%
ê 43
 
1.2%
É 28
 
0.8%
Other values (28) 148
 
4.2%
Emoticons
ValueCountFrequency (%)
😍 9
90.0%
😀 1
 
10.0%

review_comment_message
Categorical

HIGH CARDINALITY  MISSING 

Distinct35692
Distinct (%)74.9%
Missing64730
Missing (%)57.6%
Memory size878.0 KiB
Muito bom
 
254
Bom
 
200
muito bom
 
134
bom
 
117
Otimo
 
112
Other values (35687)
46825 

Length

Max length208
Median length159
Mean length70.224844
Min length1

Characters and Unicode

Total characters3345652
Distinct characters208
Distinct categories15 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30107 ?
Unique (%)63.2%

Sample

1st row1 mes de atraso na entrega !!! ultima compra q faço
2nd row1 mes de atraso na entrega !!! ultima compra q faço
3rd rowComprei dois produtos desta loja parceira da lannister e nenhum foi entregue, ja faz Mais de dois meses e NEM Dao retorno ...
4th rownao recebi o produto e nem resposta da empresa
5th rownao recebi o produto e nem resposta da empresa

Common Values

ValueCountFrequency (%)
Muito bom 254
 
0.2%
Bom 200
 
0.2%
muito bom 134
 
0.1%
bom 117
 
0.1%
Otimo 112
 
0.1%
Recomendo 109
 
0.1%
otimo 102
 
0.1%
Ok 85
 
0.1%
Ótimo 83
 
0.1%
Ótimo 82
 
0.1%
Other values (35682) 46364
41.3%
(Missing) 64730
57.6%

Length

2023-02-10T11:45:59.683366image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
o 21460
 
3.8%
produto 19929
 
3.5%
e 18952
 
3.3%
a 14323
 
2.5%
de 13703
 
2.4%
do 12468
 
2.2%
não 12346
 
2.2%
que 9780
 
1.7%
prazo 9013
 
1.6%
muito 8691
 
1.5%
Other values (19532) 430230
75.4%

Most occurring characters

ValueCountFrequency (%)
529480
15.8%
o 329883
 
9.9%
e 320649
 
9.6%
a 265180
 
7.9%
r 188408
 
5.6%
i 154357
 
4.6%
t 152570
 
4.6%
d 141384
 
4.2%
n 129971
 
3.9%
s 126390
 
3.8%
Other values (198) 1007380
30.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2526996
75.5%
Space Separator 529480
 
15.8%
Uppercase Letter 160240
 
4.8%
Other Punctuation 91258
 
2.7%
Decimal Number 20963
 
0.6%
Control 13320
 
0.4%
Dash Punctuation 937
 
< 0.1%
Close Punctuation 713
 
< 0.1%
Open Punctuation 697
 
< 0.1%
Other Symbol 675
 
< 0.1%
Other values (5) 373
 
< 0.1%

Most frequent character per category

Other Symbol
ValueCountFrequency (%)
👏 233
34.5%
👍 86
 
12.7%
😍 71
 
10.5%
° 27
 
4.0%
😉 19
 
2.8%
😆 19
 
2.8%
😡 18
 
2.7%
😘 16
 
2.4%
😁 13
 
1.9%
👎 13
 
1.9%
Other values (54) 160
23.7%
Lowercase Letter
ValueCountFrequency (%)
o 329883
13.1%
e 320649
12.7%
a 265180
10.5%
r 188408
 
7.5%
i 154357
 
6.1%
t 152570
 
6.0%
d 141384
 
5.6%
n 129971
 
5.1%
s 126390
 
5.0%
m 121057
 
4.8%
Other values (40) 597147
23.6%
Uppercase Letter
ValueCountFrequency (%)
E 18908
11.8%
O 17940
11.2%
A 16562
10.3%
P 11866
 
7.4%
R 11640
 
7.3%
C 9390
 
5.9%
M 9055
 
5.7%
N 9053
 
5.6%
S 7939
 
5.0%
T 7510
 
4.7%
Other values (31) 40377
25.2%
Other Punctuation
ValueCountFrequency (%)
. 47977
52.6%
, 26579
29.1%
! 12149
 
13.3%
/ 1753
 
1.9%
? 1550
 
1.7%
" 412
 
0.5%
: 296
 
0.3%
; 220
 
0.2%
% 176
 
0.2%
* 78
 
0.1%
Other values (5) 68
 
0.1%
Decimal Number
ValueCountFrequency (%)
1 4828
23.0%
0 4793
22.9%
2 3997
19.1%
3 1892
 
9.0%
4 1325
 
6.3%
5 1183
 
5.6%
8 910
 
4.3%
6 874
 
4.2%
7 705
 
3.4%
9 456
 
2.2%
Math Symbol
ValueCountFrequency (%)
+ 84
60.0%
= 26
 
18.6%
| 12
 
8.6%
< 10
 
7.1%
~ 3
 
2.1%
× 2
 
1.4%
> 2
 
1.4%
÷ 1
 
0.7%
Modifier Symbol
ValueCountFrequency (%)
🏻 34
33.3%
´ 26
25.5%
🏼 15
14.7%
🏽 13
 
12.7%
^ 8
 
7.8%
🏾 4
 
3.9%
` 2
 
2.0%
Control
ValueCountFrequency (%)
6650
49.9%
6650
49.9%
20
 
0.2%
Close Punctuation
ValueCountFrequency (%)
) 710
99.6%
] 3
 
0.4%
Open Punctuation
ValueCountFrequency (%)
( 691
99.1%
[ 6
 
0.9%
Other Letter
ValueCountFrequency (%)
º 26
57.8%
ª 19
42.2%
Space Separator
ValueCountFrequency (%)
529480
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 937
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 78
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2687281
80.3%
Common 658371
 
19.7%

Most frequent character per script

Common
ValueCountFrequency (%)
529480
80.4%
. 47977
 
7.3%
, 26579
 
4.0%
! 12149
 
1.8%
6650
 
1.0%
6650
 
1.0%
1 4828
 
0.7%
0 4793
 
0.7%
2 3997
 
0.6%
3 1892
 
0.3%
Other values (105) 13376
 
2.0%
Latin
ValueCountFrequency (%)
o 329883
12.3%
e 320649
11.9%
a 265180
 
9.9%
r 188408
 
7.0%
i 154357
 
5.7%
t 152570
 
5.7%
d 141384
 
5.3%
n 129971
 
4.8%
s 126390
 
4.7%
m 121057
 
4.5%
Other values (83) 757432
28.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3285002
98.2%
None 60415
 
1.8%
Emoticons 235
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
529480
16.1%
o 329883
 
10.0%
e 320649
 
9.8%
a 265180
 
8.1%
r 188408
 
5.7%
i 154357
 
4.7%
t 152570
 
4.6%
d 141384
 
4.3%
n 129971
 
4.0%
s 126390
 
3.8%
Other values (85) 946730
28.8%
None
ValueCountFrequency (%)
ã 17772
29.4%
é 10851
18.0%
á 8676
14.4%
ç 7132
11.8%
ó 5980
 
9.9%
ê 1847
 
3.1%
í 1671
 
2.8%
Ó 1490
 
2.5%
õ 901
 
1.5%
ú 850
 
1.4%
Other values (73) 3245
 
5.4%
Emoticons
ValueCountFrequency (%)
😍 71
30.2%
😉 19
 
8.1%
😆 19
 
8.1%
😡 18
 
7.7%
😘 16
 
6.8%
😁 13
 
5.5%
😊 12
 
5.1%
😀 8
 
3.4%
😩 7
 
3.0%
😃 6
 
2.6%
Other values (20) 46
19.6%
Distinct633
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size878.0 KiB
2017-12-19 00:00:00
 
516
2018-05-15 00:00:00
 
507
2018-05-19 00:00:00
 
497
2018-08-28 00:00:00
 
495
2017-12-20 00:00:00
 
489
Other values (628)
109868 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters2135068
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)< 0.1%

Sample

1st row2016-10-22 00:00:00
2nd row2016-10-22 00:00:00
3rd row2016-10-29 00:00:00
4th row2016-10-06 00:00:00
5th row2016-10-06 00:00:00

Common Values

ValueCountFrequency (%)
2017-12-19 00:00:00 516
 
0.5%
2018-05-15 00:00:00 507
 
0.5%
2018-05-19 00:00:00 497
 
0.4%
2018-08-28 00:00:00 495
 
0.4%
2017-12-20 00:00:00 489
 
0.4%
2018-05-22 00:00:00 487
 
0.4%
2018-03-29 00:00:00 486
 
0.4%
2018-08-14 00:00:00 472
 
0.4%
2018-04-12 00:00:00 467
 
0.4%
2018-05-04 00:00:00 467
 
0.4%
Other values (623) 107489
95.7%

Length

2023-02-10T11:45:59.832824image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00 112281
50.0%
2017-12-19 516
 
0.2%
2018-05-15 507
 
0.2%
2018-05-19 497
 
0.2%
2018-08-28 495
 
0.2%
2017-12-20 489
 
0.2%
2018-05-22 487
 
0.2%
2018-03-29 486
 
0.2%
2018-08-14 472
 
0.2%
2018-04-12 467
 
0.2%
Other values (625) 108047
48.1%

Most occurring characters

ValueCountFrequency (%)
0 926201
43.4%
- 224744
 
10.5%
: 224744
 
10.5%
1 195042
 
9.1%
2 179503
 
8.4%
112372
 
5.3%
8 90400
 
4.2%
7 70105
 
3.3%
3 27600
 
1.3%
5 23953
 
1.1%
Other values (3) 60404
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1573208
73.7%
Dash Punctuation 224744
 
10.5%
Other Punctuation 224744
 
10.5%
Space Separator 112372
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 926201
58.9%
1 195042
 
12.4%
2 179503
 
11.4%
8 90400
 
5.7%
7 70105
 
4.5%
3 27600
 
1.8%
5 23953
 
1.5%
4 22663
 
1.4%
6 22279
 
1.4%
9 15462
 
1.0%
Dash Punctuation
ValueCountFrequency (%)
- 224744
100.0%
Other Punctuation
ValueCountFrequency (%)
: 224744
100.0%
Space Separator
ValueCountFrequency (%)
112372
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2135068
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 926201
43.4%
- 224744
 
10.5%
: 224744
 
10.5%
1 195042
 
9.1%
2 179503
 
8.4%
112372
 
5.3%
8 90400
 
4.2%
7 70105
 
3.3%
3 27600
 
1.3%
5 23953
 
1.1%
Other values (3) 60404
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2135068
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 926201
43.4%
- 224744
 
10.5%
: 224744
 
10.5%
1 195042
 
9.1%
2 179503
 
8.4%
112372
 
5.3%
8 90400
 
4.2%
7 70105
 
3.3%
3 27600
 
1.3%
5 23953
 
1.1%
Other values (3) 60404
 
2.8%

review_answer_timestamp
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct97547
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Memory size878.0 KiB
2017-07-30 14:19:07
 
21
2018-03-12 12:46:07
 
20
2017-02-16 17:14:41
 
15
2017-12-19 14:14:16
 
15
2017-12-31 12:08:24
 
14
Other values (97542)
112287 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters2135068
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique87111 ?
Unique (%)77.5%

Sample

1st row2016-11-15 16:00:34
2nd row2016-11-15 16:00:34
3rd row2016-10-30 01:47:48
4th row2016-10-07 18:32:28
5th row2016-10-07 18:32:28

Common Values

ValueCountFrequency (%)
2017-07-30 14:19:07 21
 
< 0.1%
2018-03-12 12:46:07 20
 
< 0.1%
2017-02-16 17:14:41 15
 
< 0.1%
2017-12-19 14:14:16 15
 
< 0.1%
2017-12-31 12:08:24 14
 
< 0.1%
2018-03-03 00:44:54 14
 
< 0.1%
2017-10-24 08:44:02 13
 
< 0.1%
2018-04-26 20:37:35 13
 
< 0.1%
2018-01-26 12:22:14 12
 
< 0.1%
2017-10-19 22:55:25 12
 
< 0.1%
Other values (97537) 112223
99.9%

Length

2023-02-10T11:46:00.018599image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-05-20 761
 
0.3%
2018-05-21 673
 
0.3%
2018-05-10 565
 
0.3%
2017-12-20 438
 
0.2%
2018-04-13 428
 
0.2%
2017-12-13 415
 
0.2%
2018-05-11 406
 
0.2%
2018-08-24 396
 
0.2%
2017-12-21 394
 
0.2%
2018-08-31 390
 
0.2%
Other values (53772) 219878
97.8%

Most occurring characters

ValueCountFrequency (%)
0 359552
16.8%
1 333159
15.6%
2 284979
13.3%
- 224744
10.5%
: 224744
10.5%
8 118619
 
5.6%
112372
 
5.3%
3 104570
 
4.9%
7 96566
 
4.5%
5 88993
 
4.2%
Other values (3) 186770
8.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1573208
73.7%
Dash Punctuation 224744
 
10.5%
Other Punctuation 224744
 
10.5%
Space Separator 112372
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 359552
22.9%
1 333159
21.2%
2 284979
18.1%
8 118619
 
7.5%
3 104570
 
6.6%
7 96566
 
6.1%
5 88993
 
5.7%
4 88786
 
5.6%
6 50704
 
3.2%
9 47280
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
- 224744
100.0%
Other Punctuation
ValueCountFrequency (%)
: 224744
100.0%
Space Separator
ValueCountFrequency (%)
112372
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2135068
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 359552
16.8%
1 333159
15.6%
2 284979
13.3%
- 224744
10.5%
: 224744
10.5%
8 118619
 
5.6%
112372
 
5.3%
3 104570
 
4.9%
7 96566
 
4.5%
5 88993
 
4.2%
Other values (3) 186770
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2135068
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 359552
16.8%
1 333159
15.6%
2 284979
13.3%
- 224744
10.5%
: 224744
10.5%
8 118619
 
5.6%
112372
 
5.3%
3 104570
 
4.9%
7 96566
 
4.5%
5 88993
 
4.2%
Other values (3) 186770
8.7%

product_category_name
Categorical

HIGH CARDINALITY  MISSING 

Distinct73
Distinct (%)0.1%
Missing1598
Missing (%)1.4%
Memory size878.0 KiB
cama_mesa_banho
11137 
beleza_saude
9645 
esporte_lazer
8640 
moveis_decoracao
8331 
informatica_acessorios
7849 
Other values (68)
65172 

Length

Max length46
Median length32
Mean length14.867415
Min length3

Characters and Unicode

Total characters1646923
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmoveis_decoracao
2nd rowmoveis_decoracao
3rd rowtelefonia
4th rowbeleza_saude
5th rowbeleza_saude

Common Values

ValueCountFrequency (%)
cama_mesa_banho 11137
 
9.9%
beleza_saude 9645
 
8.6%
esporte_lazer 8640
 
7.7%
moveis_decoracao 8331
 
7.4%
informatica_acessorios 7849
 
7.0%
utilidades_domesticas 6943
 
6.2%
relogios_presentes 5950
 
5.3%
telefonia 4517
 
4.0%
ferramentas_jardim 4329
 
3.9%
automotivo 4213
 
3.7%
Other values (63) 39220
34.9%

Length

2023-02-10T11:46:00.175481image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cama_mesa_banho 11137
 
10.1%
beleza_saude 9645
 
8.7%
esporte_lazer 8640
 
7.8%
moveis_decoracao 8331
 
7.5%
informatica_acessorios 7849
 
7.1%
utilidades_domesticas 6943
 
6.3%
relogios_presentes 5950
 
5.4%
telefonia 4517
 
4.1%
ferramentas_jardim 4329
 
3.9%
automotivo 4213
 
3.8%
Other values (63) 39220
35.4%

Most occurring characters

ValueCountFrequency (%)
e 200532
12.2%
a 197630
12.0%
s 164170
10.0%
o 163030
9.9%
i 109507
 
6.6%
r 106270
 
6.5%
_ 104446
 
6.3%
t 79354
 
4.8%
c 78054
 
4.7%
m 74127
 
4.5%
Other values (18) 369803
22.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1542212
93.6%
Connector Punctuation 104446
 
6.3%
Decimal Number 265
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 200532
13.0%
a 197630
12.8%
s 164170
10.6%
o 163030
10.6%
i 109507
 
7.1%
r 106270
 
6.9%
t 79354
 
5.1%
c 78054
 
5.1%
m 74127
 
4.8%
n 56168
 
3.6%
Other values (16) 313370
20.3%
Connector Punctuation
ValueCountFrequency (%)
_ 104446
100.0%
Decimal Number
ValueCountFrequency (%)
2 265
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1542212
93.6%
Common 104711
 
6.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 200532
13.0%
a 197630
12.8%
s 164170
10.6%
o 163030
10.6%
i 109507
 
7.1%
r 106270
 
6.9%
t 79354
 
5.1%
c 78054
 
5.1%
m 74127
 
4.8%
n 56168
 
3.6%
Other values (16) 313370
20.3%
Common
ValueCountFrequency (%)
_ 104446
99.7%
2 265
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1646923
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 200532
12.2%
a 197630
12.0%
s 164170
10.0%
o 163030
9.9%
i 109507
 
6.6%
r 106270
 
6.5%
_ 104446
 
6.3%
t 79354
 
4.8%
c 78054
 
4.7%
m 74127
 
4.5%
Other values (18) 369803
22.5%

product_name_lenght
Real number (ℝ)

Distinct66
Distinct (%)0.1%
Missing1598
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean48.777583
Minimum5
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size878.0 KiB
2023-02-10T11:46:00.352256image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile29
Q142
median52
Q357
95-th percentile60
Maximum76
Range71
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.025179
Coefficient of variation (CV)0.20552841
Kurtosis0.15452858
Mean48.777583
Median Absolute Deviation (MAD)6
Skewness-0.90730409
Sum5403288
Variance100.50422
MonotonicityNot monotonic
2023-02-10T11:46:00.508553image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59 8302
 
7.4%
60 7692
 
6.8%
56 6512
 
5.8%
58 6418
 
5.7%
57 5985
 
5.3%
55 5539
 
4.9%
54 5245
 
4.7%
53 4160
 
3.7%
52 4144
 
3.7%
49 3561
 
3.2%
Other values (56) 53216
47.4%
ValueCountFrequency (%)
5 9
 
< 0.1%
6 3
 
< 0.1%
7 2
 
< 0.1%
8 4
 
< 0.1%
9 13
 
< 0.1%
10 4
 
< 0.1%
11 10
 
< 0.1%
12 37
< 0.1%
13 26
< 0.1%
14 45
< 0.1%
ValueCountFrequency (%)
76 1
 
< 0.1%
72 9
 
< 0.1%
69 1
 
< 0.1%
68 1
 
< 0.1%
67 3
 
< 0.1%
66 1
 
< 0.1%
64 163
 
0.1%
63 1253
1.1%
62 153
 
0.1%
61 233
 
0.2%
Distinct2958
Distinct (%)2.7%
Missing1598
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean786.79393
Minimum4
Maximum3992
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size878.0 KiB
2023-02-10T11:46:00.691606image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile161
Q1348
median601
Q3985
95-th percentile2120
Maximum3992
Range3988
Interquartile range (IQR)637

Descriptive statistics

Standard deviation651.6095
Coefficient of variation (CV)0.82818318
Kurtosis4.9090181
Mean786.79393
Median Absolute Deviation (MAD)295
Skewness2.0066937
Sum87156311
Variance424594.94
MonotonicityNot monotonic
2023-02-10T11:46:00.868026image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
341 688
 
0.6%
1893 633
 
0.6%
348 619
 
0.6%
903 577
 
0.5%
492 577
 
0.5%
245 541
 
0.5%
366 521
 
0.5%
236 486
 
0.4%
340 465
 
0.4%
919 421
 
0.4%
Other values (2948) 105246
93.7%
(Missing) 1598
 
1.4%
ValueCountFrequency (%)
4 6
< 0.1%
8 2
 
< 0.1%
15 1
 
< 0.1%
20 6
< 0.1%
23 1
 
< 0.1%
26 2
 
< 0.1%
27 3
 
< 0.1%
28 2
 
< 0.1%
30 8
< 0.1%
31 2
 
< 0.1%
ValueCountFrequency (%)
3992 2
 
< 0.1%
3988 1
 
< 0.1%
3985 3
< 0.1%
3976 5
< 0.1%
3963 1
 
< 0.1%
3956 3
< 0.1%
3954 2
 
< 0.1%
3950 2
 
< 0.1%
3949 1
 
< 0.1%
3948 1
 
< 0.1%

product_photos_qty
Real number (ℝ)

Distinct19
Distinct (%)< 0.1%
Missing1598
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean2.2071244
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size878.0 KiB
2023-02-10T11:46:01.020087image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum20
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7197871
Coefficient of variation (CV)0.7791981
Kurtosis4.865013
Mean2.2071244
Median Absolute Deviation (MAD)0
Skewness1.9127487
Sum244492
Variance2.9576678
MonotonicityNot monotonic
2023-02-10T11:46:01.142761image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 55943
49.8%
2 21947
 
19.5%
3 12339
 
11.0%
4 8383
 
7.5%
5 5344
 
4.8%
6 3758
 
3.3%
7 1498
 
1.3%
8 726
 
0.6%
10 342
 
0.3%
9 304
 
0.3%
Other values (9) 190
 
0.2%
(Missing) 1598
 
1.4%
ValueCountFrequency (%)
1 55943
49.8%
2 21947
 
19.5%
3 12339
 
11.0%
4 8383
 
7.5%
5 5344
 
4.8%
6 3758
 
3.3%
7 1498
 
1.3%
8 726
 
0.6%
9 304
 
0.3%
10 342
 
0.3%
ValueCountFrequency (%)
20 1
 
< 0.1%
19 2
 
< 0.1%
18 4
 
< 0.1%
17 11
 
< 0.1%
15 12
 
< 0.1%
14 6
 
< 0.1%
13 30
 
< 0.1%
12 53
 
< 0.1%
11 71
 
0.1%
10 342
0.3%

product_weight_g
Real number (ℝ)

Distinct2200
Distinct (%)2.0%
Missing18
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2090.6109
Minimum0
Maximum40425
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size878.0 KiB
2023-02-10T11:46:01.353456image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile125
Q1300
median700
Q31800
95-th percentile9750
Maximum40425
Range40425
Interquartile range (IQR)1500

Descriptive statistics

Standard deviation3748.6081
Coefficient of variation (CV)1.7930683
Kurtosis16.263327
Mean2090.6109
Median Absolute Deviation (MAD)500
Skewness3.5994489
Sum2.348885 × 108
Variance14052063
MonotonicityNot monotonic
2023-02-10T11:46:01.669935image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 6757
 
6.0%
150 5250
 
4.7%
250 4530
 
4.0%
300 4237
 
3.8%
400 3629
 
3.2%
100 3511
 
3.1%
350 3167
 
2.8%
500 2693
 
2.4%
600 2689
 
2.4%
700 2035
 
1.8%
Other values (2190) 73856
65.7%
ValueCountFrequency (%)
0 8
 
< 0.1%
2 5
 
< 0.1%
25 3
 
< 0.1%
50 948
0.8%
53 2
 
< 0.1%
54 1
 
< 0.1%
55 2
 
< 0.1%
58 1
 
< 0.1%
60 9
 
< 0.1%
61 5
 
< 0.1%
ValueCountFrequency (%)
40425 3
 
< 0.1%
30000 278
0.2%
29800 1
 
< 0.1%
29750 1
 
< 0.1%
29700 4
 
< 0.1%
29600 5
 
< 0.1%
29500 2
 
< 0.1%
29250 1
 
< 0.1%
29150 1
 
< 0.1%
29100 1
 
< 0.1%

product_length_cm
Real number (ℝ)

Distinct99
Distinct (%)0.1%
Missing18
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean30.152198
Minimum7
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size878.0 KiB
2023-02-10T11:46:01.973946image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile16
Q118
median25
Q338
95-th percentile62
Maximum105
Range98
Interquartile range (IQR)20

Descriptive statistics

Standard deviation16.139323
Coefficient of variation (CV)0.53526191
Kurtosis3.7356739
Mean30.152198
Median Absolute Deviation (MAD)8
Skewness1.7587665
Sum3387720
Variance260.47774
MonotonicityNot monotonic
2023-02-10T11:46:02.339893image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 17523
 
15.6%
20 10522
 
9.4%
30 7541
 
6.7%
17 5946
 
5.3%
18 5720
 
5.1%
25 4675
 
4.2%
19 4662
 
4.1%
40 4098
 
3.6%
22 3831
 
3.4%
50 2951
 
2.6%
Other values (89) 44885
39.9%
ValueCountFrequency (%)
7 32
 
< 0.1%
8 2
 
< 0.1%
9 4
 
< 0.1%
10 8
 
< 0.1%
11 95
 
0.1%
12 40
 
< 0.1%
13 60
 
0.1%
14 134
 
0.1%
15 201
 
0.2%
16 17523
15.6%
ValueCountFrequency (%)
105 326
0.3%
104 28
 
< 0.1%
103 45
 
< 0.1%
102 59
 
0.1%
101 107
 
0.1%
100 379
0.3%
99 35
 
< 0.1%
98 47
 
< 0.1%
97 11
 
< 0.1%
96 8
 
< 0.1%

product_height_cm
Real number (ℝ)

Distinct102
Distinct (%)0.1%
Missing18
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean16.576811
Minimum2
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size878.0 KiB
2023-02-10T11:46:02.507388image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q18
median13
Q320
95-th percentile45
Maximum105
Range103
Interquartile range (IQR)12

Descriptive statistics

Standard deviation13.437792
Coefficient of variation (CV)0.81063796
Kurtosis7.3883945
Mean16.576811
Median Absolute Deviation (MAD)6
Skewness2.2558193
Sum1862471
Variance180.57426
MonotonicityNot monotonic
2023-02-10T11:46:02.666683image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 9820
 
8.7%
15 6563
 
5.8%
20 6533
 
5.8%
12 6245
 
5.6%
11 6132
 
5.5%
2 4996
 
4.4%
8 4670
 
4.2%
4 4659
 
4.1%
5 4558
 
4.1%
16 4549
 
4.0%
Other values (92) 53629
47.7%
ValueCountFrequency (%)
2 4996
4.4%
3 2701
 
2.4%
4 4659
4.1%
5 4558
4.1%
6 3394
 
3.0%
7 4184
3.7%
8 4670
4.2%
9 3219
 
2.9%
10 9820
8.7%
11 6132
5.5%
ValueCountFrequency (%)
105 133
0.1%
104 12
 
< 0.1%
103 49
 
< 0.1%
102 10
 
< 0.1%
100 41
 
< 0.1%
99 5
 
< 0.1%
98 3
 
< 0.1%
97 2
 
< 0.1%
96 8
 
< 0.1%
95 22
 
< 0.1%

product_width_cm
Real number (ℝ)

Distinct95
Distinct (%)0.1%
Missing18
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean23.00121
Minimum6
Maximum118
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size878.0 KiB
2023-02-10T11:46:02.839944image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11
Q115
median20
Q330
95-th percentile45
Maximum118
Range112
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.707552
Coefficient of variation (CV)0.50899722
Kurtosis4.6498388
Mean23.00121
Median Absolute Deviation (MAD)6
Skewness1.7221613
Sum2584278
Variance137.06678
MonotonicityNot monotonic
2023-02-10T11:46:03.009366image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 12047
 
10.7%
11 10632
 
9.5%
15 8935
 
8.0%
16 8428
 
7.5%
30 7601
 
6.8%
12 5454
 
4.9%
13 5254
 
4.7%
14 4594
 
4.1%
18 4065
 
3.6%
40 3877
 
3.5%
Other values (85) 41467
36.9%
ValueCountFrequency (%)
6 2
 
< 0.1%
7 5
 
< 0.1%
8 28
 
< 0.1%
9 50
 
< 0.1%
10 82
 
0.1%
11 10632
9.5%
12 5454
4.9%
13 5254
4.7%
14 4594
4.1%
15 8935
8.0%
ValueCountFrequency (%)
118 7
 
< 0.1%
105 14
 
< 0.1%
104 1
 
< 0.1%
103 1
 
< 0.1%
102 2
 
< 0.1%
101 2
 
< 0.1%
100 42
< 0.1%
98 1
 
< 0.1%
97 1
 
< 0.1%
95 2
 
< 0.1%

date_achat
Categorical

Distinct616
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size878.0 KiB
2017-11-24
 
1366
2017-11-25
 
580
2017-11-27
 
480
2017-11-26
 
452
2018-08-06
 
430
Other values (611)
109064 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1123720
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row2016-09-04
2nd row2016-09-04
3rd row2016-09-05
4th row2016-09-15
5th row2016-09-15

Common Values

ValueCountFrequency (%)
2017-11-24 1366
 
1.2%
2017-11-25 580
 
0.5%
2017-11-27 480
 
0.4%
2017-11-26 452
 
0.4%
2018-08-06 430
 
0.4%
2018-08-07 429
 
0.4%
2017-11-28 428
 
0.4%
2018-05-15 420
 
0.4%
2018-05-07 417
 
0.4%
2018-05-14 412
 
0.4%
Other values (606) 106958
95.2%

Length

2023-02-10T11:46:03.154109image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-11-24 1366
 
1.2%
2017-11-25 580
 
0.5%
2017-11-27 480
 
0.4%
2017-11-26 452
 
0.4%
2018-08-06 430
 
0.4%
2018-08-07 429
 
0.4%
2017-11-28 428
 
0.4%
2018-05-15 420
 
0.4%
2018-05-07 417
 
0.4%
2018-05-14 412
 
0.4%
Other values (606) 106958
95.2%

Most occurring characters

ValueCountFrequency (%)
0 253499
22.6%
- 224744
20.0%
1 201038
17.9%
2 175507
15.6%
8 84365
 
7.5%
7 73373
 
6.5%
3 26869
 
2.4%
5 23676
 
2.1%
6 22760
 
2.0%
4 22631
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 898976
80.0%
Dash Punctuation 224744
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 253499
28.2%
1 201038
22.4%
2 175507
19.5%
8 84365
 
9.4%
7 73373
 
8.2%
3 26869
 
3.0%
5 23676
 
2.6%
6 22760
 
2.5%
4 22631
 
2.5%
9 15258
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
- 224744
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1123720
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 253499
22.6%
- 224744
20.0%
1 201038
17.9%
2 175507
15.6%
8 84365
 
7.5%
7 73373
 
6.5%
3 26869
 
2.4%
5 23676
 
2.1%
6 22760
 
2.0%
4 22631
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1123720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 253499
22.6%
- 224744
20.0%
1 201038
17.9%
2 175507
15.6%
8 84365
 
7.5%
7 73373
 
6.5%
3 26869
 
2.4%
5 23676
 
2.1%
6 22760
 
2.0%
4 22631
 
2.0%

date_initial
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size878.0 KiB
2016-09-04
112372 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1123720
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016-09-04
2nd row2016-09-04
3rd row2016-09-04
4th row2016-09-04
5th row2016-09-04

Common Values

ValueCountFrequency (%)
2016-09-04 112372
100.0%

Length

2023-02-10T11:46:03.331103image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-10T11:46:03.550545image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2016-09-04 112372
100.0%

Most occurring characters

ValueCountFrequency (%)
0 337116
30.0%
- 224744
20.0%
2 112372
 
10.0%
1 112372
 
10.0%
6 112372
 
10.0%
9 112372
 
10.0%
4 112372
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 898976
80.0%
Dash Punctuation 224744
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 337116
37.5%
2 112372
 
12.5%
1 112372
 
12.5%
6 112372
 
12.5%
9 112372
 
12.5%
4 112372
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
- 224744
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1123720
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 337116
30.0%
- 224744
20.0%
2 112372
 
10.0%
1 112372
 
10.0%
6 112372
 
10.0%
9 112372
 
10.0%
4 112372
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1123720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 337116
30.0%
- 224744
20.0%
2 112372
 
10.0%
1 112372
 
10.0%
6 112372
 
10.0%
9 112372
 
10.0%
4 112372
 
10.0%

diff_days
Real number (ℝ)

Distinct616
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean483.15701
Minimum0
Maximum729
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size878.0 KiB
2023-02-10T11:46:03.685097image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile201
Q1374
median502
Q3607
95-th percentile700
Maximum729
Range729
Interquartile range (IQR)233

Descriptive statistics

Standard deviation153.1612
Coefficient of variation (CV)0.31700089
Kurtosis-0.66009543
Mean483.15701
Median Absolute Deviation (MAD)113
Skewness-0.43714988
Sum54293320
Variance23458.354
MonotonicityIncreasing
2023-02-10T11:46:03.843459image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
446 1366
 
1.2%
447 580
 
0.5%
449 480
 
0.4%
448 452
 
0.4%
701 430
 
0.4%
702 429
 
0.4%
450 428
 
0.4%
618 420
 
0.4%
610 417
 
0.4%
617 412
 
0.4%
Other values (606) 106958
95.2%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 1
 
< 0.1%
11 3
 
< 0.1%
28 1
 
< 0.1%
29 8
 
< 0.1%
30 69
0.1%
31 54
< 0.1%
32 58
0.1%
33 49
< 0.1%
34 45
< 0.1%
ValueCountFrequency (%)
729 1
 
< 0.1%
724 16
 
< 0.1%
723 45
 
< 0.1%
722 70
 
0.1%
721 82
 
0.1%
720 75
 
0.1%
719 117
0.1%
718 161
0.1%
717 216
0.2%
716 258
0.2%

dummy
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size878.0 KiB
1
112372 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters112372
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 112372
100.0%

Length

2023-02-10T11:46:03.998831image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-10T11:46:04.116742image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 112372
100.0%

Most occurring characters

ValueCountFrequency (%)
1 112372
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 112372
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 112372
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 112372
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 112372
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 112372
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 112372
100.0%

score_freq
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size878.0 KiB
1
110545 
2
 
1496
3
 
210
5
 
63
4
 
58

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters112372
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 110545
98.4%
2 1496
 
1.3%
3 210
 
0.2%
5 63
 
0.1%
4 58
 
0.1%

Length

2023-02-10T11:46:04.235946image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-10T11:46:04.369293image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 110545
98.4%
2 1496
 
1.3%
3 210
 
0.2%
5 63
 
0.1%
4 58
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 110545
98.4%
2 1496
 
1.3%
3 210
 
0.2%
5 63
 
0.1%
4 58
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 112372
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 110545
98.4%
2 1496
 
1.3%
3 210
 
0.2%
5 63
 
0.1%
4 58
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 112372
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 110545
98.4%
2 1496
 
1.3%
3 210
 
0.2%
5 63
 
0.1%
4 58
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 112372
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 110545
98.4%
2 1496
 
1.3%
3 210
 
0.2%
5 63
 
0.1%
4 58
 
0.1%

score_rec
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size878.0 KiB
2
22735 
4
22506 
1
22477 
3
22453 
5
22201 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters112372
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 22735
20.2%
4 22506
20.0%
1 22477
20.0%
3 22453
20.0%
5 22201
19.8%

Length

2023-02-10T11:46:04.486360image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-10T11:46:04.625007image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2 22735
20.2%
4 22506
20.0%
1 22477
20.0%
3 22453
20.0%
5 22201
19.8%

Most occurring characters

ValueCountFrequency (%)
2 22735
20.2%
4 22506
20.0%
1 22477
20.0%
3 22453
20.0%
5 22201
19.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 112372
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 22735
20.2%
4 22506
20.0%
1 22477
20.0%
3 22453
20.0%
5 22201
19.8%

Most occurring scripts

ValueCountFrequency (%)
Common 112372
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 22735
20.2%
4 22506
20.0%
1 22477
20.0%
3 22453
20.0%
5 22201
19.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 112372
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 22735
20.2%
4 22506
20.0%
1 22477
20.0%
3 22453
20.0%
5 22201
19.8%

score_montant
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size878.0 KiB
5
25984 
4
22818 
3
21977 
2
21117 
1
20476 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters112372
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row2
4th row4
5th row4

Common Values

ValueCountFrequency (%)
5 25984
23.1%
4 22818
20.3%
3 21977
19.6%
2 21117
18.8%
1 20476
18.2%

Length

2023-02-10T11:46:04.750148image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-10T11:46:04.895590image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
5 25984
23.1%
4 22818
20.3%
3 21977
19.6%
2 21117
18.8%
1 20476
18.2%

Most occurring characters

ValueCountFrequency (%)
5 25984
23.1%
4 22818
20.3%
3 21977
19.6%
2 21117
18.8%
1 20476
18.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 112372
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 25984
23.1%
4 22818
20.3%
3 21977
19.6%
2 21117
18.8%
1 20476
18.2%

Most occurring scripts

ValueCountFrequency (%)
Common 112372
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 25984
23.1%
4 22818
20.3%
3 21977
19.6%
2 21117
18.8%
1 20476
18.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 112372
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 25984
23.1%
4 22818
20.3%
3 21977
19.6%
2 21117
18.8%
1 20476
18.2%

score_rfm
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1270601
Minimum3
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size878.0 KiB
2023-02-10T11:46:05.014823image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q16
median7
Q39
95-th percentile10
Maximum13
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0384348
Coefficient of variation (CV)0.28601342
Kurtosis-0.6332774
Mean7.1270601
Median Absolute Deviation (MAD)1
Skewness-0.018720136
Sum800882
Variance4.1552165
MonotonicityNot monotonic
2023-02-10T11:46:05.129334image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
7 22132
19.7%
8 18487
16.5%
6 16574
14.7%
9 14048
12.5%
5 13036
11.6%
10 9994
8.9%
4 8305
 
7.4%
11 5119
 
4.6%
3 4292
 
3.8%
12 344
 
0.3%
ValueCountFrequency (%)
3 4292
 
3.8%
4 8305
 
7.4%
5 13036
11.6%
6 16574
14.7%
7 22132
19.7%
8 18487
16.5%
9 14048
12.5%
10 9994
8.9%
11 5119
 
4.6%
12 344
 
0.3%
ValueCountFrequency (%)
13 41
 
< 0.1%
12 344
 
0.3%
11 5119
 
4.6%
10 9994
8.9%
9 14048
12.5%
8 18487
16.5%
7 22132
19.7%
6 16574
14.7%
5 13036
11.6%
4 8305
 
7.4%

Interactions

2023-02-10T11:45:47.890743image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:15.871030image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:18.271602image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:21.003027image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:23.571313image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:26.009311image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:28.259330image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:30.643526image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:33.046151image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:35.748246image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:38.466830image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:40.587153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:42.979695image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:45.401787image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:48.025383image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:16.015882image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:18.415071image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:21.219573image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:23.751488image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:26.155780image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:28.432277image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:30.891104image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:33.195458image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:35.996703image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:38.611360image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:40.737963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:43.120901image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:45.755250image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:48.168335image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:16.151712image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:18.576313image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:21.451731image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:23.924696image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:26.288757image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:28.630373image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:31.104706image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:33.406184image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:36.206757image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:38.755666image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:40.899167image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:43.261099image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:45.997587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:48.330305image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:16.308131image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:18.823983image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:21.640049image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:24.084933image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:26.442466image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:28.791362image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:31.290249image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:33.663646image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:36.370246image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:38.919241image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:41.051141image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:43.460998image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:46.197200image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:48.486812image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:16.452140image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:19.078883image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:21.819021image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:24.233845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:26.586294image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:28.953702image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:31.501752image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:33.861137image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:36.618187image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:39.064997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:41.200470image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:43.695014image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:46.370106image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:48.623743image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:16.585894image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:19.253104image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:22.013501image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:24.383875image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:26.708788image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:29.091122image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:31.640563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:33.993848image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:36.856369image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:39.209537image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:41.356386image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:43.900123image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:46.499993image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:48.795609image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:16.726415image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:19.483803image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:22.202989image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:24.541575image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:26.861797image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:29.236625image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:31.788233image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:34.142067image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:37.108713image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:39.380366image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:41.559431image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:44.045275image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:46.646228image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:49.015265image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:16.885098image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:19.743822image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:22.387710image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:24.698457image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:27.032059image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:29.389964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:31.953834image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:34.325568image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:37.370698image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:39.540773image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:41.715891image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:44.198287image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:46.868173image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:49.258067image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:17.044347image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:19.991917image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:22.610869image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:24.901459image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:27.190269image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:29.551089image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:32.121545image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:34.486284image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:37.553890image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:39.701948image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:41.903074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:44.381918image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:47.021508image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:49.510827image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:17.252893image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:20.255882image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:22.803174image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:25.070307image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:27.367668image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:29.709645image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:32.294851image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:34.650473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:37.709639image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:39.855377image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:42.112668image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:44.534971image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:47.179836image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:49.737301image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:17.485884image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:20.394424image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:22.968778image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:25.209603image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:27.503669image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:29.851038image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:32.438302image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:34.787465image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:37.852701image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:39.986434image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:42.346967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:44.670912image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:47.314648image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:49.939716image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:17.720010image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:20.537857image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:23.121600image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:25.541161image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:27.650464image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:29.989200image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:32.580496image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:35.123784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:38.003604image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:40.136902image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:42.487745image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:44.839371image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:47.455720image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:50.090306image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:17.953800image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:20.679709image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:23.274132image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:25.682093image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:27.886713image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:30.153882image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:32.721993image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:35.272187image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:38.153376image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:40.304368image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:42.666502image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:44.986091image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:47.616514image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:50.270441image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:18.148124image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:20.869735image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:23.437565image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:25.829897image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:28.104268image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:30.407166image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:32.898744image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:35.558448image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:38.319993image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:40.452113image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:42.842977image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:45.226398image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-10T11:45:47.757815image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-02-10T11:46:05.331463image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Unnamed: 0customer_zip_code_prefixorder_item_idpricefreight_valueproduct_name_lenghtproduct_description_lenghtproduct_photos_qtyproduct_weight_gproduct_length_cmproduct_height_cmproduct_width_cmdiff_daysscore_rfmcustomer_stateorder_statusreview_scoreproduct_category_namescore_freqscore_recscore_montant
Unnamed: 01.000-0.0620.0010.0090.0450.0300.057-0.011-0.055-0.079-0.010-0.0601.0000.6790.0280.0330.0560.0980.0310.9910.023
customer_zip_code_prefix-0.0621.000-0.0090.0710.4690.0160.0290.0270.0280.0130.0190.001-0.0620.0030.8960.0230.0420.0510.0300.0340.041
order_item_id0.001-0.0091.000-0.116-0.055-0.020-0.032-0.0660.0010.0070.018-0.0040.0010.1640.0000.0040.0420.0290.4650.0110.071
price0.0090.071-0.1161.0000.4340.0400.2090.0280.5140.2660.3260.2710.0090.6040.0190.0130.0110.1130.0000.0120.121
freight_value0.0450.469-0.0550.4341.0000.0340.1170.0100.4460.2830.2820.2740.0450.2920.0850.0140.0130.0930.0200.0340.129
product_name_lenght0.0300.016-0.0200.0400.0341.0000.0740.1630.0770.062-0.0550.0680.0300.0310.0120.0190.0130.1320.0260.0580.043
product_description_lenght0.0570.029-0.0320.2090.1170.0741.0000.1110.095-0.0190.132-0.0790.0570.1560.0220.0080.0130.2030.0260.0430.096
product_photos_qty-0.0110.027-0.0660.0280.0100.1630.1111.0000.0050.007-0.081-0.013-0.011-0.0210.0140.0130.0150.1510.0190.0290.026
product_weight_g-0.0550.0280.0010.5140.4460.0770.0950.0051.0000.6180.5310.620-0.0550.2920.0150.0060.0200.1990.0190.0220.171
product_length_cm-0.0790.0130.0070.2660.2830.062-0.0190.0070.6181.0000.2480.631-0.0790.1170.0130.0090.0180.2590.0230.0460.140
product_height_cm-0.0100.0190.0180.3260.282-0.0550.132-0.0810.5310.2481.0000.340-0.0100.2100.0160.0140.0180.2780.0370.0350.149
product_width_cm-0.0600.001-0.0040.2710.2740.068-0.079-0.0130.6200.6310.3401.000-0.0600.1290.0120.0030.0130.2950.0290.0410.141
diff_days1.000-0.0620.0010.0090.0450.0300.057-0.011-0.055-0.079-0.010-0.0601.0000.6790.0290.0670.0590.0980.0310.8070.024
score_rfm0.6790.0030.1640.6040.2920.0310.156-0.0210.2920.1170.2100.1290.6791.0000.0240.0120.0380.1140.3350.4260.430
customer_state0.0280.8960.0000.0190.0850.0120.0220.0140.0150.0130.0160.0120.0290.0241.0000.0250.0490.0340.0340.0360.041
order_status0.0330.0230.0040.0130.0140.0190.0080.0130.0060.0090.0140.0030.0670.0120.0251.0000.1320.0290.0070.0300.014
review_score0.0560.0420.0420.0110.0130.0130.0130.0150.0200.0180.0180.0130.0590.0380.0490.1321.0000.0540.0510.0430.041
product_category_name0.0980.0510.0290.1130.0930.1320.2030.1510.1990.2590.2780.2950.0980.1140.0340.0290.0541.0000.0750.1270.219
score_freq0.0310.0300.4650.0000.0200.0260.0260.0190.0190.0230.0370.0290.0310.3350.0340.0070.0510.0751.0000.0210.079
score_rec0.9910.0340.0110.0120.0340.0580.0430.0290.0220.0460.0350.0410.8070.4260.0360.0300.0430.1270.0211.0000.018
score_montant0.0230.0410.0710.1210.1290.0430.0960.0260.1710.1400.1490.1410.0240.4300.0410.0140.0410.2190.0790.0181.000

Missing values

2023-02-10T11:45:50.836565image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-10T11:45:52.260025image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-02-10T11:45:53.636148image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0customer_idcustomer_unique_idcustomer_zip_code_prefixcustomer_citycustomer_stateorder_idorder_statusorder_purchase_timestamporder_approved_atorder_delivered_carrier_dateorder_delivered_customer_dateorder_estimated_delivery_dateorder_item_idproduct_idseller_idshipping_limit_datepricefreight_valuereview_idreview_scorereview_comment_titlereview_comment_messagereview_creation_datereview_answer_timestampproduct_category_nameproduct_name_lenghtproduct_description_lenghtproduct_photos_qtyproduct_weight_gproduct_length_cmproduct_height_cmproduct_width_cmdate_achatdate_initialdiff_daysdummyscore_freqscore_recscore_montantscore_rfm
0008c5351a6aca1c1589a38f244edeee9db7d76e111c89f7ebf14761390f0f7d1769309boa vistaRR2e7a8482f6fb09756ca50c10d7bfc047shipped2016-09-04 21:15:192016-10-07 13:18:032016-10-18 13:14:51NaN2016-10-20 00:00:002f293394c72c9b5fafd7023301fc21fc21554a68530182680ad5c8b042c3ab5632016-10-26 18:25:1932.9031.67cef1ee03ded4d6272894a2eead6e13281NaN1 mes de atraso na entrega !!! ultima compra q faço2016-10-22 00:00:002016-11-15 16:00:34moveis_decoracao41.0754.03.01800.032.06.028.02016-09-042016-09-040.011135
1108c5351a6aca1c1589a38f244edeee9db7d76e111c89f7ebf14761390f0f7d1769309boa vistaRR2e7a8482f6fb09756ca50c10d7bfc047shipped2016-09-04 21:15:192016-10-07 13:18:032016-10-18 13:14:51NaN2016-10-20 00:00:001c1488892604e4ba5cff5b4eb4d5954001554a68530182680ad5c8b042c3ab5632016-10-26 18:25:1939.9931.67cef1ee03ded4d6272894a2eead6e13281NaN1 mes de atraso na entrega !!! ultima compra q faço2016-10-22 00:00:002016-11-15 16:00:34moveis_decoracao59.0426.02.01400.032.06.028.02016-09-042016-09-040.011135
22683c54fc24d40ee9f8a6fc179fd9856c4854e9b3feff728c13ee5fc7d1547e9299025passo fundoRSe5fa5a7210941f7d56d0208e4e071d35canceled2016-09-05 00:15:342016-10-07 13:17:15NaNNaN2016-10-28 00:00:001f3c2d01a84c947b078e32bbef0718962a425f92c199eb576938df686728acd202016-09-19 00:15:3459.5015.56a93139d9d1314158c080e3db7e79618b1NaNComprei dois produtos desta loja parceira da lannister e nenhum foi entregue, ja faz Mais de dois meses e NEM Dao retorno ...2016-10-29 00:00:002016-10-30 01:47:48telefonia42.0381.01.0700.025.02.025.02016-09-052016-09-041.011124
3386dc2ffce2dfff336de2f386a786e574830d5b7aaa3b6f1e9ad63703bec97d2314600sao joaquim da barraSPbfbd0f9bdef84302105ad712db648a6cdelivered2016-09-15 12:16:382016-09-15 12:16:382016-11-07 17:11:532016-11-09 07:47:382016-10-04 00:00:0035a6b04657a4c5ee34285d1e4619a96b4ecccfa2bb93b34a3bf033cc5d1dcdc692016-09-19 23:11:3344.992.836916ca4502d6d3bfd39818759d55d5361NaNnao recebi o produto e nem resposta da empresa2016-10-06 00:00:002016-10-07 18:32:28beleza_saude34.01036.01.01000.016.016.016.02016-09-152016-09-0411.011146
4486dc2ffce2dfff336de2f386a786e574830d5b7aaa3b6f1e9ad63703bec97d2314600sao joaquim da barraSPbfbd0f9bdef84302105ad712db648a6cdelivered2016-09-15 12:16:382016-09-15 12:16:382016-11-07 17:11:532016-11-09 07:47:382016-10-04 00:00:0025a6b04657a4c5ee34285d1e4619a96b4ecccfa2bb93b34a3bf033cc5d1dcdc692016-09-19 23:11:3344.992.836916ca4502d6d3bfd39818759d55d5361NaNnao recebi o produto e nem resposta da empresa2016-10-06 00:00:002016-10-07 18:32:28beleza_saude34.01036.01.01000.016.016.016.02016-09-152016-09-0411.011146
5586dc2ffce2dfff336de2f386a786e574830d5b7aaa3b6f1e9ad63703bec97d2314600sao joaquim da barraSPbfbd0f9bdef84302105ad712db648a6cdelivered2016-09-15 12:16:382016-09-15 12:16:382016-11-07 17:11:532016-11-09 07:47:382016-10-04 00:00:0015a6b04657a4c5ee34285d1e4619a96b4ecccfa2bb93b34a3bf033cc5d1dcdc692016-09-19 23:11:3344.992.836916ca4502d6d3bfd39818759d55d5361NaNnao recebi o produto e nem resposta da empresa2016-10-06 00:00:002016-10-07 18:32:28beleza_saude34.01036.01.01000.016.016.016.02016-09-152016-09-0411.011146
66b106b360fe2ef8849fbbd056f777b4d50eb1ee9dba87f5b36b4613a65074337c2975sao pauloSP71303d7e93b399f5bcd537d124c0bcfacanceled2016-10-02 22:07:522016-10-06 15:50:56NaNNaN2016-10-25 00:00:001d2998d7ced12f83f9b832f33cf6507b625e6ffe976bd75618accfe16cefcbd0d2016-10-21 16:19:54100.009.3434d62feeefaf60ef6ff7204af19fe1091NaNNaN2016-10-27 00:00:002016-10-27 23:40:51bebes32.0561.01.0500.018.018.018.02016-10-022016-09-0428.011135
77b8cf418e97ae795672d326288dfab7a78d3a54507421dbd2ce0a1d58046826e013185hortolandiaSPd207cc272675637bfed0062edffd0818delivered2016-10-03 22:06:032016-10-04 10:28:072016-10-21 14:23:372016-10-31 11:07:422016-11-23 00:00:001107177bf61755f05c604fe57e02467d6cca3071e3e9bb7d12640c9fbe23013062016-10-21 16:23:06119.9013.56444d04d7ca0131b3b40619f81d0facd51NaNBoa tarde o produto veio correto, porem a entrega é pessima precisei buscar em outra cidade.\r\nAcho absurdo se comprei pele internet e paguei o frete nao entendi porque nao entregar em minha residencia2016-11-01 00:00:002016-11-02 16:52:00moveis_decoracao55.0130.01.02050.040.011.034.02016-10-032016-09-0429.011146
88355077684019f7f60a031656bd7262b832ea3bdedab835c3aa6cb68ce66565ef4106sao pauloSP3b697a20d9e427646d92567910af6d57delivered2016-10-03 09:44:502016-10-06 15:50:542016-10-23 14:02:132016-10-26 14:02:132016-10-27 00:00:0013ae08df6bcbfe23586dd431c40bddbb7522620dcb18a6b31cd7bdf73665113a92016-10-21 16:27:2029.9015.569fe0f66724df77fd63fcc0c94a3239784NaNFacilidade em manusear o aparelho.2016-10-26 00:00:002016-10-30 21:32:15relogios_presentes63.01642.03.0300.016.016.016.02016-10-032016-09-0429.011113
99dc607dc98d6a11d5d04d9f2a70aa6c3410e89fd8e5c745f81bec101207ba4d7d35162ipatingaMGef1b29b591d31d57c0d7337460dd83c9delivered2016-10-03 22:51:302016-10-04 10:28:192016-10-21 14:23:362016-11-01 15:14:452016-11-25 00:00:001bfce5e847034e1fbbc1ed0bff6a372c7cca3071e3e9bb7d12640c9fbe23013062016-10-21 16:22:3374.9017.37a36150969fe6bd09ed011dd78aeb01891NaNDemora na entrega, detestei o atendimento e NUNCA mais compro. baratheon quando vendia por eles mesmos era bem melhor!2016-11-02 00:00:002016-11-03 12:04:24moveis_decoracao55.0248.01.01800.040.08.030.02016-10-032016-09-0429.011135
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112362112362e450a297a7bc6839ceb0cf1a2377fa027a22d14aa3c3599238509ddca4b93b015863sao pauloSP52018484704db3661b98ce838612b507delivered2018-08-29 12:25:592018-08-29 12:35:172018-08-29 13:38:002018-08-30 22:48:272018-09-03 00:00:001777798445efd625458a90c13f3b3e6e75f2684dab12e59f83bef73ae57724e452018-08-31 12:35:1763.909.207a11bf826668febba0800ec35884958c1Muito frágil !!!Achei o produto muito pequeno e onde fica a "tela " com os botões do microondas sai com facilidade. Não tem um bom encaixe...2018-08-31 00:00:002018-09-21 13:54:38brinquedos52.0711.02.01500.028.022.018.02018-08-292016-09-04724.011528
112363112363b8c19e70d00f6927388e4f31c923d7850c6d7218d5f3fa14514fd298652699939625sao bernardo do campoSP912859fef5a0bd5059b6d48fa79d121adelivered2018-08-29 09:48:092018-08-29 10:04:162018-08-29 19:01:002018-08-30 23:28:522018-09-04 00:00:0019865c67a74684715521d1e70226cce0bfa1c13f2614d7b5c4749cbc52fecda942018-09-03 10:04:16169.808.45728cca2fecca64b4f4cbf212bdc873195NaNNaN2018-08-31 00:00:002018-09-01 10:11:33relogios_presentes52.0629.01.0290.019.013.013.02018-08-292016-09-04724.0115410
112364112364448945bc713d98b6726e82eda6249b9eafbcfd0b9c5233e7ccc73428526fbb5212243sao jose dos camposSPbee12e8653a04e76786e8891cfb6330adelivered2018-08-29 08:46:112018-08-29 09:04:102018-08-29 13:03:002018-08-30 21:54:452018-09-11 00:00:0048d4dac6177fb8134f26fb4c5cc6c0affc70c1b0d8ca86052f45a432a38b739582018-09-06 09:04:1091.557.906b390a593dc8e574cc660146cf5366ec5NaNNaN2018-08-31 00:00:002018-09-01 16:57:41beleza_saude52.01649.01.0250.022.05.015.02018-08-292016-09-04724.0115511
11236511236556b1ac2855cc6d7950b4ffa6a9b41b0d0421e7a23f21e5d54efed456aedbc51313322saltoSPd03ca98f59480e7e76c71fa83ecd8fb6delivered2018-08-29 11:06:112018-08-29 11:24:022018-08-29 17:46:002018-08-30 23:56:542018-09-04 00:00:00106601c3059e35a3bf65e72f2fd2ac6266b90f847357d8981edd79a1eb1bf0acb2018-08-31 11:24:02109.909.525c7d6b979aed548db0901cf9ffe3a2dc5NaNNaN2018-08-31 00:00:002018-09-03 13:58:54alimentos60.0523.04.0750.028.011.013.02018-08-292016-09-04724.0115410
112366112366c4c66f47534e09a03fc7a878a9eda5eaf80013faf776e37bcea7634d59c2181e4716sao pauloSPc84d88553f9878bf2c7ecda2eb211ecedelivered2018-08-29 08:25:342018-08-29 08:44:132018-08-29 20:01:002018-08-30 16:56:242018-09-03 00:00:00124bc2932a12c983f8e76d828b65cf39b5a413ade68e8f8d93071a7f52a64cb9e2018-08-31 08:44:1365.009.212eac2da79f635ecf63675f93e9d952525MuitoO produto chegou mais rápido do que o esperado2018-08-31 00:00:002018-09-03 11:32:32beleza_saude12.02411.01.0380.017.011.013.02018-08-292016-09-04724.011528
112367112367448945bc713d98b6726e82eda6249b9eafbcfd0b9c5233e7ccc73428526fbb5212243sao jose dos camposSPbee12e8653a04e76786e8891cfb6330adelivered2018-08-29 08:46:112018-08-29 09:04:102018-08-29 13:03:002018-08-30 21:54:452018-09-11 00:00:0058d4dac6177fb8134f26fb4c5cc6c0affc70c1b0d8ca86052f45a432a38b739582018-09-06 09:04:1091.557.906b390a593dc8e574cc660146cf5366ec5NaNNaN2018-08-31 00:00:002018-09-01 16:57:41beleza_saude52.01649.01.0250.022.05.015.02018-08-292016-09-04724.0115511
112368112368e60df9449653a95af4549bbfcb18a6eb5c58de6fb80e93396e2f35642666b69380045curitibaPR0b223d92c27432930dfe407c6aea3041delivered2018-08-29 14:18:232018-08-29 14:31:072018-08-29 15:29:002018-08-30 16:24:552018-09-04 00:00:0022b4472df15512a2825ae86fd9ae7933567bf6941ba2f1fa1d02c375766bc3e532018-08-31 14:30:19209.0046.486c50d16eb583d5db7e841b77e89b70455NaNNaN2018-08-31 00:00:002018-10-24 16:27:36moveis_cozinha_area_de_servico_jantar_e_jardim44.0112.01.013550.048.049.049.02018-08-292016-09-04724.0115511
112369112369448945bc713d98b6726e82eda6249b9eafbcfd0b9c5233e7ccc73428526fbb5212243sao jose dos camposSPbee12e8653a04e76786e8891cfb6330adelivered2018-08-29 08:46:112018-08-29 09:04:102018-08-29 13:03:002018-08-30 21:54:452018-09-11 00:00:0038d4dac6177fb8134f26fb4c5cc6c0affc70c1b0d8ca86052f45a432a38b739582018-09-06 09:04:1091.557.906b390a593dc8e574cc660146cf5366ec5NaNNaN2018-08-31 00:00:002018-09-01 16:57:41beleza_saude52.01649.01.0250.022.05.015.02018-08-292016-09-04724.0115511
112370112370448945bc713d98b6726e82eda6249b9eafbcfd0b9c5233e7ccc73428526fbb5212243sao jose dos camposSPbee12e8653a04e76786e8891cfb6330adelivered2018-08-29 08:46:112018-08-29 09:04:102018-08-29 13:03:002018-08-30 21:54:452018-09-11 00:00:0018d4dac6177fb8134f26fb4c5cc6c0affc70c1b0d8ca86052f45a432a38b739582018-09-06 09:04:1091.557.906b390a593dc8e574cc660146cf5366ec5NaNNaN2018-08-31 00:00:002018-09-01 16:57:41beleza_saude52.01649.01.0250.022.05.015.02018-08-292016-09-04724.0115511
1123711123714b7decb9b58e2569548b8b4c8e20e8d7ff22e30958c13ffe219db7d711e8f5642989sao pauloSP54282e97f61c23b78330c15b154c867dshipped2018-09-03 09:06:572018-09-03 17:40:062018-09-04 15:25:00NaN2018-09-06 00:00:001b98992ea80b467987a7fbb88e7f2076a25be943a321c8938947bdaabca979a902018-09-05 17:30:54145.0021.466efce984242ca6456d74750810733a031NaNNao recebi2018-08-31 00:00:002018-08-31 09:51:47moveis_cozinha_area_de_servico_jantar_e_jardim58.0576.01.010400.016.068.032.02018-09-032016-09-04729.0115410